The document summarizes an automated system that generates music slideshows using web images based on lyrics analysis. The system extracts query words from song lyrics to retrieve candidate images from Flickr. It then selects optimal images for each lyric line based on social tag analysis and an "impression score" that measures how well images match the overall theme of the song lyrics. Evaluation experiments found the system can automatically generate impressive music slideshows for any input song.
This document describes a mood-based music player application that uses facial expression recognition to determine a user's mood and generate an appropriate music playlist. The application first scans a user's device to analyze audio files and extract features to categorize songs by genre or mood. It then uses the Viola-Jones algorithm on a captured photo of the user's face to detect their expression and deduce their mood. The recognized mood is then used to select a pre-generated playlist that matches. The goal is to automate the process of playlist generation based on a user's current emotional state to provide a more personalized music listening experience.
IRJET- A Personalized Music Recommendation SystemIRJET Journal
This document describes a personalized music recommendation system that uses collaborative filtering and convolutional neural networks. The system provides three types of recommendations: popularity-based recommendations based on the most popular songs among all users, item-based recommendations of similar songs based on a user's listening history using collaborative filtering, and genre-based recommendations based on the genres of songs a user has listened to previously as determined by a convolutional neural network classifier. The system was tested on a dataset of music listening logs and audio files and evaluated based on its ability to provide personalized music recommendations to users.
Query by Example of Speaker Audio Signals using Power Spectrum and MFCCsIJECEIAES
Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%.
Spotify Stream Prediction using Regression ModelsIRJET Journal
This document summarizes a research paper that used regression models to predict the number of streams for songs on Spotify based on song attributes. The researchers collected a Spotify dataset and performed exploratory data analysis to identify influential variables. They used linear regression, random forest, ridge regression, and lasso regression models and found that random forest achieved the highest prediction accuracy of 97.48%. Key song attributes like loudness and energy were found to most impact streams. The research aimed to understand what makes songs popular and to approximately predict streams for new songs based on their attributes.
Effective Results of Searching Song for Keyword-Based Retrieval Systems Using...Suraj Ligade
Nowadays advent of new technologies users can use the internet and play any type of music from anywhere, anytime searching song it very easy. Searching songs, videos and movies by using keyword-based searching method it’s very most natural and simple way to searching it. In proposed system the recommendation is based on users profile and users search history is very most challenging task in this system. Hybrid recommendation mechanism to search song by giving text input by user as well as it recommends user by doing effective ranking of the results obtained. User can easily search songs, videos, movies through text recognition system. In order to perform a search, the users can enter the song title or artist. The keyword-search based method is providing better efficiency and accuracy for result. The main function of proposed system is to perform song searching based on text input giving by the user is easy and effective searching of songs. In proposed system we use the information from a user’s search history as well as the common properties of users with similar backgrounds. The keyword-based retrieval system is very simple and easy and is very helpful to all people. The proposed system uses the combination of songs, videos as well as movies are more intensive and efficient method of search using text query. The pattern of sorting text and audio information is based on music library and re-ranking of music search history. The proposed system is providing the security of our system is very innovative and challenging task in this system. Proposed system providing suggestions and does not need to enter the proper keyword to search songs, videos, movies.
IRJET- Implementation of Emotion based Music Recommendation System using SVM ...IRJET Journal
This document describes a proposed emotion-based music recommendation system that uses facial expression recognition and an SVM algorithm. The system aims to suggest songs to users based on their detected emotion state in order to save them time in manually selecting songs. It would use computer vision components like OpenCV to determine a user's emotion from facial expressions. Once an emotion is recognized, the SVM model would suggest a song matching that emotion. The system aims to automate mood-based playlist creation and improve the music enjoyment experience. It outlines the methodology, including using OpenCV for facial recognition, an SVM algorithm to classify emotions detected, natural language processing for chatbot responses, and IFTTT for response recording.
Mehfil : Song Recommendation System Using Sentiment DetectedIRJET Journal
This document describes a song recommendation system called Mehfil that uses sentiment analysis to recommend songs based on a user's detected mood. It has three main modules: sentiment analysis using facial recognition and emotion detection on images via a deep learning model, music recommendation by classifying songs based on audio features and assigning mood labels, and integration using the Spotify API to generate personalized playlists based on the detected sentiment. The system aims to make creating mood-based playlists easier by analyzing a user's facial expression in real-time with their webcam to infer their mood and select an appropriate playlist of songs. It discusses the technologies used like Haar Cascade for face detection, MobileNetV2 for sentiment classification, and the Spotify API for music metadata and
This document describes a mood-based music player application that uses facial expression recognition to determine a user's mood and generate an appropriate music playlist. The application first scans a user's device to analyze audio files and extract features to categorize songs by genre or mood. It then uses the Viola-Jones algorithm on a captured photo of the user's face to detect their expression and deduce their mood. The recognized mood is then used to select a pre-generated playlist that matches. The goal is to automate the process of playlist generation based on a user's current emotional state to provide a more personalized music listening experience.
IRJET- A Personalized Music Recommendation SystemIRJET Journal
This document describes a personalized music recommendation system that uses collaborative filtering and convolutional neural networks. The system provides three types of recommendations: popularity-based recommendations based on the most popular songs among all users, item-based recommendations of similar songs based on a user's listening history using collaborative filtering, and genre-based recommendations based on the genres of songs a user has listened to previously as determined by a convolutional neural network classifier. The system was tested on a dataset of music listening logs and audio files and evaluated based on its ability to provide personalized music recommendations to users.
Query by Example of Speaker Audio Signals using Power Spectrum and MFCCsIJECEIAES
Search engine is the popular term for an information retrieval (IR) system. Typically, search engine can be based on full-text indexing. Changing the presentation from the text data to multimedia data types make an information retrieval process more complex such as a retrieval of image or sounds in large databases. This paper introduces the use of language and text independent speech as input queries in a large sound database by using Speaker identification algorithm. The method consists of 2 main processing first steps, we separate vocal and non-vocal identification after that vocal be used to speaker identification for audio query by speaker voice. For the speaker identification and audio query by process, we estimate the similarity of the example signal and the samples in the queried database by calculating the Euclidian distance between the Mel frequency cepstral coefficients (MFCC) and Energy spectrum of acoustic features. The simulations show that the good performance with a sustainable computational cost and obtained the average accuracy rate more than 90%.
Spotify Stream Prediction using Regression ModelsIRJET Journal
This document summarizes a research paper that used regression models to predict the number of streams for songs on Spotify based on song attributes. The researchers collected a Spotify dataset and performed exploratory data analysis to identify influential variables. They used linear regression, random forest, ridge regression, and lasso regression models and found that random forest achieved the highest prediction accuracy of 97.48%. Key song attributes like loudness and energy were found to most impact streams. The research aimed to understand what makes songs popular and to approximately predict streams for new songs based on their attributes.
Effective Results of Searching Song for Keyword-Based Retrieval Systems Using...Suraj Ligade
Nowadays advent of new technologies users can use the internet and play any type of music from anywhere, anytime searching song it very easy. Searching songs, videos and movies by using keyword-based searching method it’s very most natural and simple way to searching it. In proposed system the recommendation is based on users profile and users search history is very most challenging task in this system. Hybrid recommendation mechanism to search song by giving text input by user as well as it recommends user by doing effective ranking of the results obtained. User can easily search songs, videos, movies through text recognition system. In order to perform a search, the users can enter the song title or artist. The keyword-search based method is providing better efficiency and accuracy for result. The main function of proposed system is to perform song searching based on text input giving by the user is easy and effective searching of songs. In proposed system we use the information from a user’s search history as well as the common properties of users with similar backgrounds. The keyword-based retrieval system is very simple and easy and is very helpful to all people. The proposed system uses the combination of songs, videos as well as movies are more intensive and efficient method of search using text query. The pattern of sorting text and audio information is based on music library and re-ranking of music search history. The proposed system is providing the security of our system is very innovative and challenging task in this system. Proposed system providing suggestions and does not need to enter the proper keyword to search songs, videos, movies.
IRJET- Implementation of Emotion based Music Recommendation System using SVM ...IRJET Journal
This document describes a proposed emotion-based music recommendation system that uses facial expression recognition and an SVM algorithm. The system aims to suggest songs to users based on their detected emotion state in order to save them time in manually selecting songs. It would use computer vision components like OpenCV to determine a user's emotion from facial expressions. Once an emotion is recognized, the SVM model would suggest a song matching that emotion. The system aims to automate mood-based playlist creation and improve the music enjoyment experience. It outlines the methodology, including using OpenCV for facial recognition, an SVM algorithm to classify emotions detected, natural language processing for chatbot responses, and IFTTT for response recording.
Mehfil : Song Recommendation System Using Sentiment DetectedIRJET Journal
This document describes a song recommendation system called Mehfil that uses sentiment analysis to recommend songs based on a user's detected mood. It has three main modules: sentiment analysis using facial recognition and emotion detection on images via a deep learning model, music recommendation by classifying songs based on audio features and assigning mood labels, and integration using the Spotify API to generate personalized playlists based on the detected sentiment. The system aims to make creating mood-based playlists easier by analyzing a user's facial expression in real-time with their webcam to infer their mood and select an appropriate playlist of songs. It discusses the technologies used like Haar Cascade for face detection, MobileNetV2 for sentiment classification, and the Spotify API for music metadata and
A Review on Song Recommendation TechniquesIRJET Journal
This document reviews techniques for song recommendation, specifically as it relates to matching songs to a singer's vocal abilities. It discusses traditional recommendation systems that focus on recommending songs based on a listener's interests but do not consider the singer's skills. The document then summarizes several content-based recommendation techniques, including using a singer's preferences to retrieve similar songs, integrating multiple acoustic features to generate music descriptors, and using chord progressions to represent songs. It also discusses evaluating singer performance, feature extraction/selection, and ranking methods to match songs to a singer's vocal competence. The goal is to recommend songs that singers can perform well, rather than just songs listeners enjoy, to improve singing performance.
This document describes a music player app called Moodify that automatically generates playlists based on a user's detected mood or emotion. The app uses facial recognition technology to analyze a user's expression via their device's camera. It then selects songs from its database that match the identified emotion like happy, sad, angry or neutral. This provides a more personalized listening experience for users compared to manually browsing songs. The system is designed to be faster and more accurate at determining mood than previous music recommendation algorithms.
survey on Hybrid recommendation mechanism to get effective ranking results fo...Suraj Ligade
These days clients are having exclusive
requirements towards advancements, they need to hunt tunes
in such circumstances where they are not ready to recall tunes
title or melody related points of interest. Recovery of music or
melodies substance is one of the hardest errands and testing
work in the field of Music Information Retrieval (MIR). There
are different looking techniques created and executed, yet
these seeking strategies are no more ready to inquiry tunes
which required by the clients and confronting different issues
like programmed playlist creation, music suggestion or music
pursuit are connected issues. In past framework client seek
the tune with the assistance of tune title, craftsman name and
whatever other related points of interest so this strategy is
exceptionally tedious. To beat this issue singing so as to look
tune or murmuring a segment of it is the most regular
approach to seek the tune. This hunt strategy is the most
helpful when client don't have entry to sound gadget or client
can't review the traits of the tune such as tune title, name of
craftsman, name of collection. In proposed framework client
have not stress over recalling the tune data and this technique
is not tedious. In this strategy we utilize the data from a
client's hunt history and in addition the normal properties of
client's comparative foundations. Cross breed proposal
component utilizes the substance construct recovery
framework situated in light of utilization of the sound data
such as tone, pitch, mood. This component used to get exact
result to the client. The more imperative idea is clients ready
to work their gadgets without manual information orders by
hand. It is simple and basic system to perform music look.
The document discusses developing a model to compose monophonic world music using deep learning techniques. It proposes using a bi-axial recurrent neural network with one axis representing time and the other representing musical notes. The network will be trained on a dataset of MIDI files describing pitch, timing, and velocity of notes. It will also incorporate information from music theory on scales, chords, and other elements extracted from sheet music files. The goal is to generate unique musical sequences while adhering to music theory rules. The model aims to address the problem of composing long durations of background music for public spaces in an automated way.
AI&BigData Lab 2016. Игорь Костюк: Как приручить музыкальную рекомендательную...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Это — рекомендательная система. Если взглянуть на нее со стороны, то она крепко застряла между Collaborative filtering и Content-based filtering. Используются рекомендательные системы уже давно, но рекомендации все еще не идеальны. Обычно проблемы — это выбор технологий или там фреймворка… А у нас — cold-start problem, semantic gap и др.!
The document discusses a music recommendation system project that uses content-based filtering and collaborative filtering techniques. Content-based filtering extracts features from songs to find similar songs based on acoustic content. Collaborative filtering matches users based on similar tastes and ratings to generate recommendations. The project has developed a website using Ruby on Rails for the frontend and Python for the backend. Current work involves completing the collaborative filtering approach and exploring query by humming algorithms.
A novel Image Retrieval System using an effective region based shape represen...CSCJournals
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and attempt to retrieve shapes from the database that have a similar shape. This paper introduces a novel and efficient shape matching approach for the automatic identification of real world objects. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape signatures and the similarity estimation of pairs of objects considering the information extracted from the segmentation process and shape signature. We compute a 1D shape signature function from a region shape and use it for region shape representation and retrieval through similarity estimation. The proposed region shape feature is much more efficient to compute than other region shape techniques invariant to image transformation.
Marking Human Labeled Training Facial Images Searching and Utilizing Annotati...IRJET Journal
This document proposes a system to annotate faces in videos with name labels by matching faces in video frames to a database of labeled facial images. The key steps are:
1) Extract frames from the input video.
2) Recognize faces in each frame and match them to labeled faces in the database to retrieve name annotations.
3) Associate the retrieved names to the corresponding faces in each frame.
Conditional random fields are used to model relationships between detected faces and names to determine the optimal face labeling that maximizes the joint probability. A within-video face labeling algorithm is presented using belief propagation on a constructed graph. Preliminary implementation results demonstrate taking a video as input and extracting its frames.
AUTOMATED MUSIC MAKING WITH RECURRENT NEURAL NETWORKJennifer Roman
The document describes an automated music making system using recurrent neural networks that allows users to generate music online by specifying genres, types, and length, with the goal of making music generation more accessible; it details the architecture of the system and the recurrent neural network model used to generate MIDI music files based on training from existing music datasets; and several challenges of music generation are discussed along with related work on algorithmic music generation.
Igor Kostiuk “Как приручить музыкальную рекомендательную систему”Dakiry
This document discusses different approaches for training music recommender systems using deep learning techniques. It describes using collaborative filtering to obtain latent representations of songs from user listening data. Convolutional neural networks can then be trained to predict these latent factors directly from mel-spectrograms of audio clips. This allows the system to recommend new songs without requiring a user's listening history. The document outlines the development process, including extracting mel-spectrograms from audio, performing weighted matrix factorization on user data, and using the neural network to map spectrograms to the latent factors.
11. Efficient Image Based Searching for Improving User Search Image GoalsINFOGAIN PUBLICATION
The analysis of a user search goals for a query can be very useful in improving search engine relevance and the user experience. Although the research on inferring by user goals and intents for text search has received much attention, so small has been proposed for image search. In this paper, we propose to leverage click session information, which will indicate by high correlations among the clicked images in a session in a user click-through logs, and combine it with the clicked image visual information for inferring the user image-search goals. Since the click session information can serve as past users’ implicit guidance for the clustering the images, more precise user search goals can be obtained. The two strategies are proposed because of combine image visual information for the click session information. Furthermore a classification risk based on approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on the popular commercial search engine for demonstrate the effectiveness of the proposed method
IRJET- Analysis of Music Recommendation System using Machine Learning Alg...IRJET Journal
This document analyzes different machine learning algorithms that can be used to build a music recommendation system. It first discusses how machine learning and data mining are used to extract patterns from large music datasets. It then analyzes different classification, clustering, and association algorithms that are suitable for a music recommendation system. Specifically, it applies two algorithms (Random Forest and XGBClassifier) to a music dataset and compares their performance at different training/test data splits. It finds that Random Forest achieved the highest accuracy of 75% when the split was 75% training and 25% testing data. In conclusion, ensemble techniques like Random Forest can improve the accuracy of music recommendation over single algorithms.
Tom Collins is a PhD student at the Centre for Research in Computing studying how current methods for pattern discovery in music can be improved and integrated into an automated composition system. He is improving pattern discovery algorithms in two ways: 1) developing a new formula to rate discovered patterns based on empirical user ratings, and 2) creating a new algorithm called SIACT that outperforms existing algorithms at finding translational patterns based on benchmarks set by a music analyst. His presentation will demonstrate these improvements and how they are incorporated into a user interface.
This document describes a music search engine and lyrics advisor application. It allows users to search for a song title by entering lyrics or the song name. It also detects and tags strong language in lyrics, and provides advice on songs containing vulgar words. The system is designed with a search algorithm, strong word detection algorithm, and database to store lyrics. It is built with PHP, Dreamweaver for the interface, and uses a database like phpMyAdmin. The search engine matches input to lyrics and song titles in the database. The strong word detection compares lyrics to a list of vulgar words and provides advice if a match is found. Limitations include issues with special characters and other languages due to PHP and UTF-8 encoding problems.
Music Recommendation System using Euclidean, Cosine Similarity, Correlation D...IRJET Journal
This document describes a music recommendation system that uses various distance and similarity algorithms like Euclidean distance, cosine similarity, and correlation distance. It integrates these recommendation models with a user-friendly web interface built using the Flask framework. The system is evaluated based on how accurately and relevantly it can recommend songs. In summary, the project created an effective music recommendation system that utilizes different similarity metrics and Flask for web integration.
The Application Programming Interface restricts
the types of queries that the Web service can answer. For
instance, a Web service might provide a method that returns the
books of a given author in fast manner, but it might not provide a
method that returns the authors of a given book. If the user asks
for the author of some specific book, then the Web service cannot
be called – even though the underlying database might have the
preferred piece of information, this scenario is called asymmetry.
This asymmetry is particularly problematic if the service is used
in a Web service orchestration system. In this survey, we propose
to use on-the-fly information extraction (IE).IE used to collect
values, and then the value can be used as parameter Bindings for
the Web service. This survey shows how the information
extraction can be integrated into a Web service orchestration
system. The proposed approach is fully implemented in a
prototype called Search Using Services and Information
Extraction (SUSIE). Real-life data and services are used to
demonstrate the practical viability and good performance of our
approach
Knn a machine learning approach to recognize a musical instrumentIJARIIT
An outline is provided of a proposed system to recognize musical instruments using machine learning techniques. The system first extracts features from audio files using the MIR toolbox in Matlab. It then uses a hybrid feature selection method and vector quantization to identify instruments. Specifically, the key audio descriptors are selected and feature vectors are generated and matched to standard vectors to classify the instrument. The k-nearest neighbors algorithm is used for classification. Preliminary results show the system can accurately recognize instruments based on extracted acoustic features.
The document describes a proposed music recommendation system that uses machine learning. It would employ both collaborative filtering and content-based filtering approaches. Collaborative filtering would analyze users' listening histories to find people with similar music preferences and recommend songs listened to by similar users. Content-based filtering would examine music attributes like genre, tempo, and lyrics to recommend similar songs. The system is intended to provide personalized music suggestions to users and potentially improve their music discovery experience. It outlines the key steps of collecting data from music streaming services, preprocessing the data, extracting music features, and evaluating the recommendations against baseline algorithms.
IRJET- Music Genre Classification using Machine Learning Algorithms: A Compar...IRJET Journal
This document presents research on classifying music genres using machine learning algorithms. The researchers built multiple classification models using the Free Music Archive dataset and compared the models' performance in predicting genre accuracy. Some models were trained on mel-spectrograms of songs and their audio features, while others used only spectrograms. The researchers found that a convolutional neural network model trained solely on spectrograms achieved the highest accuracy among the tested models. The goal of the research was to develop a machine learning approach for automatic music genre classification that performs better than existing methods.
The document discusses treatments for sleep apnea. CPAP is considered the most effective treatment as it reduces nighttime sleeplessness compared to oral appliances. The discussion will consider arguments for and against CPAP and oral appliances as treatments for sleep apnea. Pros of CPAP include its high effectiveness while cons include discomfort. Oral appliances have the pros of comfort but the con of lower effectiveness compared to CPAP.
How To Write Better Essays (12 Best Tips)Dustin Pytko
The document provides steps for requesting writing assistance from HelpWriting.net. It explains that users must first create an account, then complete a request form providing instructions, sources, and deadline. Writers will bid on the request, and the user can choose a writer based on qualifications. The user can request revisions until satisfied with the paper.
More Related Content
Similar to Automated Music Slideshow Generation Using Web Images Based On Lyrics
A Review on Song Recommendation TechniquesIRJET Journal
This document reviews techniques for song recommendation, specifically as it relates to matching songs to a singer's vocal abilities. It discusses traditional recommendation systems that focus on recommending songs based on a listener's interests but do not consider the singer's skills. The document then summarizes several content-based recommendation techniques, including using a singer's preferences to retrieve similar songs, integrating multiple acoustic features to generate music descriptors, and using chord progressions to represent songs. It also discusses evaluating singer performance, feature extraction/selection, and ranking methods to match songs to a singer's vocal competence. The goal is to recommend songs that singers can perform well, rather than just songs listeners enjoy, to improve singing performance.
This document describes a music player app called Moodify that automatically generates playlists based on a user's detected mood or emotion. The app uses facial recognition technology to analyze a user's expression via their device's camera. It then selects songs from its database that match the identified emotion like happy, sad, angry or neutral. This provides a more personalized listening experience for users compared to manually browsing songs. The system is designed to be faster and more accurate at determining mood than previous music recommendation algorithms.
survey on Hybrid recommendation mechanism to get effective ranking results fo...Suraj Ligade
These days clients are having exclusive
requirements towards advancements, they need to hunt tunes
in such circumstances where they are not ready to recall tunes
title or melody related points of interest. Recovery of music or
melodies substance is one of the hardest errands and testing
work in the field of Music Information Retrieval (MIR). There
are different looking techniques created and executed, yet
these seeking strategies are no more ready to inquiry tunes
which required by the clients and confronting different issues
like programmed playlist creation, music suggestion or music
pursuit are connected issues. In past framework client seek
the tune with the assistance of tune title, craftsman name and
whatever other related points of interest so this strategy is
exceptionally tedious. To beat this issue singing so as to look
tune or murmuring a segment of it is the most regular
approach to seek the tune. This hunt strategy is the most
helpful when client don't have entry to sound gadget or client
can't review the traits of the tune such as tune title, name of
craftsman, name of collection. In proposed framework client
have not stress over recalling the tune data and this technique
is not tedious. In this strategy we utilize the data from a
client's hunt history and in addition the normal properties of
client's comparative foundations. Cross breed proposal
component utilizes the substance construct recovery
framework situated in light of utilization of the sound data
such as tone, pitch, mood. This component used to get exact
result to the client. The more imperative idea is clients ready
to work their gadgets without manual information orders by
hand. It is simple and basic system to perform music look.
The document discusses developing a model to compose monophonic world music using deep learning techniques. It proposes using a bi-axial recurrent neural network with one axis representing time and the other representing musical notes. The network will be trained on a dataset of MIDI files describing pitch, timing, and velocity of notes. It will also incorporate information from music theory on scales, chords, and other elements extracted from sheet music files. The goal is to generate unique musical sequences while adhering to music theory rules. The model aims to address the problem of composing long durations of background music for public spaces in an automated way.
AI&BigData Lab 2016. Игорь Костюк: Как приручить музыкальную рекомендательную...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
Это — рекомендательная система. Если взглянуть на нее со стороны, то она крепко застряла между Collaborative filtering и Content-based filtering. Используются рекомендательные системы уже давно, но рекомендации все еще не идеальны. Обычно проблемы — это выбор технологий или там фреймворка… А у нас — cold-start problem, semantic gap и др.!
The document discusses a music recommendation system project that uses content-based filtering and collaborative filtering techniques. Content-based filtering extracts features from songs to find similar songs based on acoustic content. Collaborative filtering matches users based on similar tastes and ratings to generate recommendations. The project has developed a website using Ruby on Rails for the frontend and Python for the backend. Current work involves completing the collaborative filtering approach and exploring query by humming algorithms.
A novel Image Retrieval System using an effective region based shape represen...CSCJournals
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and attempt to retrieve shapes from the database that have a similar shape. This paper introduces a novel and efficient shape matching approach for the automatic identification of real world objects. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape signatures and the similarity estimation of pairs of objects considering the information extracted from the segmentation process and shape signature. We compute a 1D shape signature function from a region shape and use it for region shape representation and retrieval through similarity estimation. The proposed region shape feature is much more efficient to compute than other region shape techniques invariant to image transformation.
Marking Human Labeled Training Facial Images Searching and Utilizing Annotati...IRJET Journal
This document proposes a system to annotate faces in videos with name labels by matching faces in video frames to a database of labeled facial images. The key steps are:
1) Extract frames from the input video.
2) Recognize faces in each frame and match them to labeled faces in the database to retrieve name annotations.
3) Associate the retrieved names to the corresponding faces in each frame.
Conditional random fields are used to model relationships between detected faces and names to determine the optimal face labeling that maximizes the joint probability. A within-video face labeling algorithm is presented using belief propagation on a constructed graph. Preliminary implementation results demonstrate taking a video as input and extracting its frames.
AUTOMATED MUSIC MAKING WITH RECURRENT NEURAL NETWORKJennifer Roman
The document describes an automated music making system using recurrent neural networks that allows users to generate music online by specifying genres, types, and length, with the goal of making music generation more accessible; it details the architecture of the system and the recurrent neural network model used to generate MIDI music files based on training from existing music datasets; and several challenges of music generation are discussed along with related work on algorithmic music generation.
Igor Kostiuk “Как приручить музыкальную рекомендательную систему”Dakiry
This document discusses different approaches for training music recommender systems using deep learning techniques. It describes using collaborative filtering to obtain latent representations of songs from user listening data. Convolutional neural networks can then be trained to predict these latent factors directly from mel-spectrograms of audio clips. This allows the system to recommend new songs without requiring a user's listening history. The document outlines the development process, including extracting mel-spectrograms from audio, performing weighted matrix factorization on user data, and using the neural network to map spectrograms to the latent factors.
11. Efficient Image Based Searching for Improving User Search Image GoalsINFOGAIN PUBLICATION
The analysis of a user search goals for a query can be very useful in improving search engine relevance and the user experience. Although the research on inferring by user goals and intents for text search has received much attention, so small has been proposed for image search. In this paper, we propose to leverage click session information, which will indicate by high correlations among the clicked images in a session in a user click-through logs, and combine it with the clicked image visual information for inferring the user image-search goals. Since the click session information can serve as past users’ implicit guidance for the clustering the images, more precise user search goals can be obtained. The two strategies are proposed because of combine image visual information for the click session information. Furthermore a classification risk based on approach is also proposed for automatically selecting the optimal number of search goals for a query. Experimental results based on the popular commercial search engine for demonstrate the effectiveness of the proposed method
IRJET- Analysis of Music Recommendation System using Machine Learning Alg...IRJET Journal
This document analyzes different machine learning algorithms that can be used to build a music recommendation system. It first discusses how machine learning and data mining are used to extract patterns from large music datasets. It then analyzes different classification, clustering, and association algorithms that are suitable for a music recommendation system. Specifically, it applies two algorithms (Random Forest and XGBClassifier) to a music dataset and compares their performance at different training/test data splits. It finds that Random Forest achieved the highest accuracy of 75% when the split was 75% training and 25% testing data. In conclusion, ensemble techniques like Random Forest can improve the accuracy of music recommendation over single algorithms.
Tom Collins is a PhD student at the Centre for Research in Computing studying how current methods for pattern discovery in music can be improved and integrated into an automated composition system. He is improving pattern discovery algorithms in two ways: 1) developing a new formula to rate discovered patterns based on empirical user ratings, and 2) creating a new algorithm called SIACT that outperforms existing algorithms at finding translational patterns based on benchmarks set by a music analyst. His presentation will demonstrate these improvements and how they are incorporated into a user interface.
This document describes a music search engine and lyrics advisor application. It allows users to search for a song title by entering lyrics or the song name. It also detects and tags strong language in lyrics, and provides advice on songs containing vulgar words. The system is designed with a search algorithm, strong word detection algorithm, and database to store lyrics. It is built with PHP, Dreamweaver for the interface, and uses a database like phpMyAdmin. The search engine matches input to lyrics and song titles in the database. The strong word detection compares lyrics to a list of vulgar words and provides advice if a match is found. Limitations include issues with special characters and other languages due to PHP and UTF-8 encoding problems.
Music Recommendation System using Euclidean, Cosine Similarity, Correlation D...IRJET Journal
This document describes a music recommendation system that uses various distance and similarity algorithms like Euclidean distance, cosine similarity, and correlation distance. It integrates these recommendation models with a user-friendly web interface built using the Flask framework. The system is evaluated based on how accurately and relevantly it can recommend songs. In summary, the project created an effective music recommendation system that utilizes different similarity metrics and Flask for web integration.
The Application Programming Interface restricts
the types of queries that the Web service can answer. For
instance, a Web service might provide a method that returns the
books of a given author in fast manner, but it might not provide a
method that returns the authors of a given book. If the user asks
for the author of some specific book, then the Web service cannot
be called – even though the underlying database might have the
preferred piece of information, this scenario is called asymmetry.
This asymmetry is particularly problematic if the service is used
in a Web service orchestration system. In this survey, we propose
to use on-the-fly information extraction (IE).IE used to collect
values, and then the value can be used as parameter Bindings for
the Web service. This survey shows how the information
extraction can be integrated into a Web service orchestration
system. The proposed approach is fully implemented in a
prototype called Search Using Services and Information
Extraction (SUSIE). Real-life data and services are used to
demonstrate the practical viability and good performance of our
approach
Knn a machine learning approach to recognize a musical instrumentIJARIIT
An outline is provided of a proposed system to recognize musical instruments using machine learning techniques. The system first extracts features from audio files using the MIR toolbox in Matlab. It then uses a hybrid feature selection method and vector quantization to identify instruments. Specifically, the key audio descriptors are selected and feature vectors are generated and matched to standard vectors to classify the instrument. The k-nearest neighbors algorithm is used for classification. Preliminary results show the system can accurately recognize instruments based on extracted acoustic features.
The document describes a proposed music recommendation system that uses machine learning. It would employ both collaborative filtering and content-based filtering approaches. Collaborative filtering would analyze users' listening histories to find people with similar music preferences and recommend songs listened to by similar users. Content-based filtering would examine music attributes like genre, tempo, and lyrics to recommend similar songs. The system is intended to provide personalized music suggestions to users and potentially improve their music discovery experience. It outlines the key steps of collecting data from music streaming services, preprocessing the data, extracting music features, and evaluating the recommendations against baseline algorithms.
IRJET- Music Genre Classification using Machine Learning Algorithms: A Compar...IRJET Journal
This document presents research on classifying music genres using machine learning algorithms. The researchers built multiple classification models using the Free Music Archive dataset and compared the models' performance in predicting genre accuracy. Some models were trained on mel-spectrograms of songs and their audio features, while others used only spectrograms. The researchers found that a convolutional neural network model trained solely on spectrograms achieved the highest accuracy among the tested models. The goal of the research was to develop a machine learning approach for automatic music genre classification that performs better than existing methods.
Similar to Automated Music Slideshow Generation Using Web Images Based On Lyrics (20)
The document discusses treatments for sleep apnea. CPAP is considered the most effective treatment as it reduces nighttime sleeplessness compared to oral appliances. The discussion will consider arguments for and against CPAP and oral appliances as treatments for sleep apnea. Pros of CPAP include its high effectiveness while cons include discomfort. Oral appliances have the pros of comfort but the con of lower effectiveness compared to CPAP.
How To Write Better Essays (12 Best Tips)Dustin Pytko
The document provides steps for requesting writing assistance from HelpWriting.net. It explains that users must first create an account, then complete a request form providing instructions, sources, and deadline. Writers will bid on the request, and the user can choose a writer based on qualifications. The user can request revisions until satisfied with the paper.
How To Write A 500-Word Essay About - Agnew TextDustin Pytko
This document provides instructions for writing a 500-word essay and summarizing a court case on assisted suicide. It explains a 5-step process for getting writing help from the site: 1) Create an account; 2) Complete an order form with instructions and deadline; 3) Review writer bids and choose one; 4) Review the paper and authorize payment; 5) Request revisions until satisfied. It also summarizes the 1997 Supreme Court case Washington v. Glucksberg, which upheld a ban on assisted suicide.
Sample On Project Management By Instant EDustin Pytko
This document discusses different scholars' attempts to answer the question of what causes economic growth over history. It mentions Ibn Khaldun in the 14th century proposing that economic growth occurs through the expansion of towns to cities, increasing demand and specialization of labor. It also references Adam Smith, David Hume, Karl Marx, and later economists like Alfred Marshall and Robert Solow who studied this question. While many prominent thinkers have tried to address this challenging issue, it remains an important but still unresolved question in economics and academic studies.
The document discusses wanting a country with equal rights for everyone and the opportunity for everyone to have a say in government through representative democracy. Representative democracy allows citizens to elect officials to represent their interests and make decisions on their behalf, valuing everyone having a voice in the political process. Ensuring equal rights and participation in the democratic system are core values that the document advocates for in the type of country it wants.
The Creative Spirit Graffiti Challenge 55 Graffiti Art LettDustin Pytko
The document discusses how to request writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account, 2) Complete an order form providing instructions and deadline, 3) Review bids from writers and select one, 4) Review the completed paper and authorize payment, 5) Request revisions until satisfied. It emphasizes that original, high-quality content is guaranteed, with refunds for plagiarized work.
My First Day At College - GCSE English - Marked BDustin Pytko
The document discusses file sharing and web piracy. It provides a brief history of file sharing and considers arguments from both sides of the issue. Some see file sharing as unethical and equivalent to theft, as it costs media industries billions in lost profits each year. Others view it as fair use. The document aims to summarize the issue and present the author's personal stance.
💋 The Help Movie Analysis Essay. The Help Film Anal.pdfDustin Pytko
The document provides instructions for requesting assignment writing help from the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and choose one. 4) Review the completed paper and authorize payment. 5) Request revisions until satisfied. The document emphasizes that original, high-quality content will be provided, with a full refund option for plagiarized work.
Essay Writing Step-By-Step A Newsweek Education PrDustin Pytko
Here are the key points about commercialization of art in China:
- Commercialization of art has become a global trend in the 21st century, making artworks more visible to the public through auctions, galleries, and media.
- Commercialism has had a significant impact on the Chinese art market in recent decades, as the 20th century and contemporary art genres have grown.
- As China's economy has rapidly developed, a new class of wealthy art collectors and patrons has emerged, fueling demand for Chinese artworks.
- Major international auction houses like Sotheby's and Christie's have expanded into the Chinese market, holding high-profile sales of Chinese paintings and sculptures.
- Some argue commercialization
Writing A Dialogue Paper. How To Format Dialogue (Includes ExamplDustin Pytko
The document provides instructions for creating an account and submitting a request for writing assistance on the HelpWriting.net website. It outlines a 5-step process: 1) Create an account with a password and email, 2) Complete an order form with instructions and deadline, 3) Review bids from writers and select one, 4) Review the completed paper and authorize payment, 5) Request revisions until satisfied. The document emphasizes that original, high-quality content is guaranteed or a full refund will be provided.
Princess Dakota lives in the far away world of The Enchantments but finds herself transported to Winfred, West Virginia through time travel. As a princess by day and superhero by night, Dakota possesses special abilities that will help her navigate this unfamiliar world. However, she must first figure out how she arrived in Winfred and how to return home.
Upon arriving in Winfred, Dakota realizes she is in an unknown place and time. She will have to use her powers of flight, strength, and energy blasts discreetly to blend in and investigate how she traveled through time and space. Dakota hopes to find
Essay On Importance Of Education In 150 Words. ShDustin Pytko
The document discusses the similarities and differences between four ancient river valley civilizations: Egypt along the Nile River, the Indus Valley civilization in modern-day Pakistan, Mesopotamia between the Tigris and Euphrates Rivers in modern-day Iraq, and China along the Yellow River. All four societies developed agriculture, technology, cities, and forms of government or rule, though they differed in their political and social structures, with Egypt and Mesopotamia having kings who ruled as gods and China having an emperor, while the Indus Valley is more mysterious without a clear ruler. Gender roles also varied between focusing on agriculture for men and childrearing for women.
Types Of Essays We Can Write For You Types Of Essay, EDustin Pytko
1) Math anxiety can negatively impact academic and career opportunities. Being afraid of math subjects one from pursuing important STEM fields.
2) With practice and exposure, math anxiety is very treatable. Learning coping strategies and gaining confidence with support can help reduce anxiety over time.
3) Understanding where math anxiety comes from, such as past experiences, can help address its root causes. Reframing negative thoughts about math ability can make the subject seem less frightening.
Lined Paper For Writing Notebook Paper Template,Dustin Pytko
1. Voyageurs National Park is located in northern Minnesota and spans over 218,000 acres of land and water. It was established in 1975 to protect the cultural history and unique ecosystem of the area.
2. The park receives an average of 25-28 inches of precipitation annually and contains rare plants and animals like loons, bears, and moose. Recreational activities include hiking, snowmobiling, skiing, and camping.
3. The park protects fire-dependent forests, wetlands, and over 900 species of wildflowers and berries. While the landscape has changed over thousands of years, plant life has faced the greatest challenges in the last century from factors like fire, wind, and human activity
Research Paper Executive Summary Synopsis WritinDustin Pytko
The document discusses how coalitions play a major role in shaping American politics. Coalitions between parties, officials, and organizations influence major decisions made in the U.S. Over time, allied groups tend to experience changes. The 2016 election year featured new coalition formations and breakdowns. Parties strive to maintain or gain power by taking steps to tip the scales in their favor through coalitions.
Uk Best Essay Service. Order Best Essays In UKDustin Pytko
This document provides instructions for ordering essays from Uk Best Essay Service. It outlines a 5-step process: 1) Create an account, 2) Complete an order form providing instructions and deadline, 3) Review bids from writers and choose one, 4) Review the paper and authorize payment, 5) Request revisions until satisfied. It emphasizes the company's promise to provide original, high-quality content or offer a full refund.
What Is The Body Of A Paragraph. How To Write A Body Paragraph For ADustin Pytko
The story introduces An-Imal Island, home to animals of all sizes. Mr. and Mrs. Rabbit Stalk have their 13th child, a baby bunny boy. They name him Hopper.
My Handwriting , . Online assignment writing service.Dustin Pytko
The document discusses inflammatory bowel disease (IBD), which involves chronic inflammation of the digestive tract. IBD consists mainly of ulcerative colitis and Crohn's disease, causing symptoms like diarrhea, pain, fatigue, and weight loss. Treatment for IBD aims to reduce inflammation and can include medications like anti-inflammatory drugs, immune system suppressors, corticosteroids, aminosalicylates, and antibiotics. While IBD has no cure, proper medical and surgical care can help control symptoms and potentially lead to mucosal healing.
How To Stay Calm During Exam And Term Paper WritiDustin Pytko
The document provides instructions for students on how to request writing assistance from HelpWriting.net, including creating an account, completing an order form with instructions and deadline, and reviewing bids from writers to select one and authorize payment after receiving a draft of the paper. The process aims to ensure students receive original, high-quality content and can request revisions until satisfied.
Image Result For Fundations Letter Formation Page FundationsDustin Pytko
The document outlines a 5 step process for making an ethical decision when seeking writing help from HelpWriting.net, including creating an account, completing an order form, having writers bid on the request, choosing a writer, and authorizing payment after reviewing and revising the paper. It emphasizes ensuring original, high-quality content and providing revisions to satisfy the customer.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
2. outdoor scenery. However, no evidence has been pro-
vided that such images are optimal for music slideshows.
A naive approach is to use the top-ranked images in the
search results for the image selection. In this case though,
highly ranked images are expected to be selected repeti-
tively for the same query, hence, the same image may be
used for different songs with similar lyrics. Therefore, this
approach is expected to generate slideshows with a lack
of diversity, which may cause boredom for system users.
3. SYSTEM CONFIGURATION
The configuration of the proposed system is illustrated in
Figure 1. The system selects one image for each lyric line
of the input song. The selected image is displayed on the
slideshow application (Figure 2) during music play. Im-
ages for the slideshow are collected from Flickr, a highly
popular photograph sharing site [11], by using the Flickr
API. As illustrated in Figure 1, we assume that a database
which contains songs with their corresponding lyrics and
timing information is prepared beforehand, as in the case
of karaoke systems.
Figure 1. System configuration
The process flow of the system consists of the following
three steps.
1. Candidate Image Retrieval
This step extracts a candidate set of images per lyric
line, by selecting appropriate query words from each
line of the lyrics of the input song.
2. Image Selection
This step selects an image from the previously ex-
tracted candidate image set for each line, to compose
the slide show.
3. Synchronized Playback
Selected images for each line are displayed with the
song, according to the prepared timing information.
Figure 2. A screenshot of the proposed system
The following section explains the slideshow genera-
tion method, namely the candidate image retrieval and
image selection steps, in detail.
4. SLIDESHOW GENERATION METHOD
4.1 Candidate Image Retrieval
In this step, the system generates a query (set of words)
for each lyric line of the input song. The image search re-
sult from Flickr, obtained by the generated query is uti-
lized as the candidate image set for the lyric line. The
query is generated by analyzing the frequency of query
words that are applied to the images in the search result,
as social tags. This method is based on the hypothesis that,
query words which are frequently used as social tags in
Flickr have a significant meaning in the web image data-
base, thus are expected to be effective to retrieve images
which are expressive of the song lyrics. This method ex-
tracts the optimum combination of query words for each
lyric line, based on the following three ideas:
Words used in a lyric line should be prioritized, since
such words accurately represent the content of the line.
The query should be composed with as many words as
possible, since such queries are more specific than sin-
gle word queries, thus should result in more accurate
image retrieval.
Multiple words within a query tend to co-occur as im-
age social tags.
4.1.1 Process Flow of Social Tag-Based Query Selection
Let Nline(li) represent the set of nouns used at the i-th line
of the lyrics, Npara(li) represent the nouns used in the pa-
ragraph which contains the i-th line, and Nall(m) represent
the word set which describes the general impression of
song m (hereafter referred to as “general impression
words”, details explained in Section 4.1.2). Furthermore,
when W expresses the set of words used as the query for
ARTIST
TITLE
LYRICS
64
11th International Society for Music Information Retrieval Conference (ISMIR 2010)
3. the Flickr API, let DF(W) (Document Frequency)
represent the number of images in the search results, and
UF(W) (User Frequency) represent the number of unique
users (counted by the user ID information of the Flickr
images) in the search results.
The proposed method extracts candidate query words
for the i-th line in lyrics of music piece m, from Nline(li)
and Npara(li). Words which have DF or UF value less than
a pre-defined threshold are omitted. The thresholds for
DF and UF are empirically set as 40 and 10, respectively.
Next, let P(Nline(li)) express the power set of Nline(li):
P(Nline(li))={Wline,1, Wline,2, … Wline,x}, where Wline,x ex-
presses the x-th set of words in P(Nline(li)). From
P(Nline(li)), Wmax is selected under the condition that
DF(Wmax) is not zero and that |Wmax| is the highest in
P(Nline(li)), where |W| expresses the number of words in
W. If more than one Wmax can be selected, the set which
has the highest UF(Wmax) is selected. In this way, Wmax is
regarded as the set of queries for the i-th line, Qline(li).
Then, in order to maximize the number of query words
(which is assumed to reduce the number of candidate im-
ages, and improve search accuracy), we expand the query
by using words in Npara(li). Namely, expanded sets of
words, which are composed of the power set of Npara(li),
plus the previously derived Qline(li) are generated as
P’(Npara(li))={Wpara,1+Qline(li), Wpara,2+Qline(li), … Wpa-
ra,y+Qline(li)} = {W’para,1, W’para,2, … W’para,y}. Then, in
the same way as explained above, W’max is selected from
P’(Npara(li)).
Finally, by sending the all elements of W’max under the
condition of ‘AND’ combination to Flickr, the system re-
trieves the candidate images for each line. If W’max has no
elements, Nall(m) is used as the query.
4.1.2 Estimation of General Song Impression
As mentioned above, Nall(m) is the general impression
word set, i.e., a set of words which expresses the collec-
tive impression of song m. This word set can be used for
lyric lines from which no effective query words could be
extracted. Furthermore, the general impression word set is
also effective to generate slideshows with a sense of unity,
as will be described in the next section.
The general impression of a song is estimated by text-
based classification based on its entire lyrics. Namely,
song classifiers are preliminarily constructed by SVM
[12] for each of the categories showed in Table 1. The
categories are divided into three concepts: Season,
Weather, and Time. Each concept consists of several cat-
egories. The concepts/categories in Table 1 are selected
because they all represent important aspects of song lyrics,
and are expressed by discriminative words. For the clas-
sifier, we used the software SVMlight
[13] with a linear
kernel for learning. Here, lyrics have been vectorized by
TF*IDF, and the training data for the classifiers learning
have been obtained by a manually collected database of
Japanese pop songs with human-applied labels. If the
classifier determines that a song m is positive for its re-
spective category, the name of the category is added to
Nall(m). Note that multiple words may be included in
Nall(m).
Concepts Category labels
Season Spring, Summer, Autumn, Winter
Weather Sunny, Cloudy, Rain, Snow, Rainbow
Time Morning, Daytime, Evening, Night
Table 1. Concepts and category labels for describing
general impression of music.
4.2 Image Selection
The next step is to select an image to compose the slide-
show from the candidate image set for each lyric line. We
propose an image selection method based on an impres-
sion score, which represents strength of association be-
tween the image and the general impression words of the
input song. Consideration of the impression score is ex-
pected to select images that are more fitting to the overall
theme of the input song, thus increases the sense of unity
among the images which compose the slideshow.
4.2.1 Relevant Tag Extraction Based on Co-occurrence
Probability
Relevant tags for calculating the impression score are ex-
tracted based on co-occurrence probability of social tags
on Flickr. In this paper, the co-occurrence probability is
calculated based on UF instead of DF, since there are
many tags with unusually high DF on Flickr, due to users
who upload many images with the exact same tag set,
while UF is more robust to the effect of such user beha-
vior.
The relevance score between a general impression
word nall ∈ Nall(m), and a given tag t, is calculated by the
co-occurrence probability of t and nall, and also the im-
pression words which belong to the same concept as nall.
For example, when the relevance score between “sum-
mer” and tag t is calculated, the same score for all other
general impression words in the “Season” concept, i.e.,
“spring”, “autumn”, and “winter”, are also calculated. In
this way, it is possible to extract tags which have specifi-
cally high relevance to nall, and decrease the score of gen-
erally popular tags, i.e., words which co-occur frequently
with many other words.
The co-occurrence score between general impression
word nall and tag t, CoScore(t,nall), is defined as:
( ) ( )
( )
all
all
all
n
UF
n
t
UF
n
t
CoScore
∩
=
, (1)
65
11th International Society for Music Information Retrieval Conference (ISMIR 2010)
4. Then, the relevance score R between nall and t is defined
as:
( ) ( )
( )
wgt
C
n
t
P
n
t
CoScore
n
t
R all
n
n
C
n
all
all ×
−
−
=
∑
≠
∈
1
|
,
,
,
(2)
where C is the set of general impression words which be-
long to the same concept of nall. For example, when nall =
“spring”, C = {“spring”, “summer”, “autumn”, “winter”},
since “spring” belongs to the “Season” concept. In the
definition of the relevance score in Eq.(2), the first term
increases the score of tags which have high co-occurrence
probability with nall. Subtraction of the second term de-
creases the score of tags with high co-occurrence proba-
bility of impression words which belong to the same con-
cept as nall. Note that wgt is a coefficient to adjust the im-
pact of the second term. This coefficient is set to 3, em-
pirically.
Based on Eq.(2), the relevance score between each
general impression word, and all tags which co-occur
with the general impression word, are calculated. Tags
whose relevance scores are over 0.024, and UF value ex-
ceeds 5, are regarded as relevant tags of each impression
word.
4.2.2 Definition of Impression Score
For image selection, we calculate the impression score for
all images in the candidate image set, based on the tags
applied to the image, and the above relevance score. The
object of this method is to select images with tags which
have high relevance to the general impression words of
the input song. As a result of this process, the impression
score of images with “noisy” tags, i.e., tags with low re-
levance to the general impression of the input song, will
be degraded.
The impression score of image i is determined by
( )
( )
( )
( )
( )
∑
∑
∈
∩
∈
∩
−
=
m
N
n all
related
i
i
n
T
T
t
all
all
all
all
related
i
n
T
T
T
n
t
R
i
score
,
(3)
where Ti is the set of tags applied to image i, Trelated(nall)
is the relevance tag set of general impression word nall,
and R(t, nall) is the relevance score between nall and tag t.
This impression score is computed for each candidate
image, and the image with the highest score is selected to
be displayed with its respective lyric line, during the sli-
deshow.
4.3 Image Transition Timing Adjustment
In the proposed system, the images obtained per lyric line
are displayed in synchronization with each line during the
song playback. Adequate usage of the line information
leads to natural image transition during the slideshow,
since lines represent a semantic unit in the lyrics. Howev-
er, display duration of each image may be too short/long
when using the line information naively. For example, in
a rap song with many lines, the image display time maybe
too short, so that users may not be able to comprehend the
images in the slideshow. On the other hand, in a slow bal-
lad song, images may be displayed for a long time, which
may cause boredom.
In order to improve the overall quality of the slideshow,
we propose an image transition timing adjustment method,
which adjusts the display time of images according to the
duration of each lyric line. In this process, we first esti-
mate the typical duration time of images in a song. Then,
the line of lyrics is “combined” or “divided”, based on the
difference of the duration of the line and the typical dura-
tion time of the input song. In the “combining” process,
lines with short duration time are combined with their ad-
jacent lines, and a single image is displayed for the com-
bined set of lines. In the “dividing” process, lines with
long duration time are “divided” into plural sub-lines, and
an image is to be displayed along with each sub-line.
The process flow for image transition timing adjust-
ment consists of the following steps.
1. Typical duration time of song m is calculated from the
lyrics data. Namely, the mode value of the line dura-
tion time is used as the typical image duration time Im.
2. Lines which have less than 4 [sec] duration time are
“combined” with the next line. If there is no next line,
it is “combined” with the previous line. However, lines
are “combined” only if they belong to the same para-
graph. An image is retrieved for the newly combined
line.
3. A line which has more than 12 [sec] duration time is
“divided” equally. The number of divisions is con-
trolled so that approximate duration time of the new
“divided” line is equivalent to Im. When the line is “di-
vided” into n lines, n images are displayed from the
candidate image set, which is retrieved based on the
lyrics of the original line.
4. Interlude sections (which generally have no lyrics) are
divided by the same process as step 3. The general im-
pression words are used as query for image retrieval.
5. EXPERIMENTS
5.1 Outline
In order to evaluate the quality of the proposed method,
we have conducted a subjective evaluation experiment.
This experiment compares the proposed method with oth-
er conventional methods, by asking 42 subjects to rate the
slideshows generated by all methods. The subjects are
asked to view the music slideshows of the same song,
which are generated by the proposed and comparative
methods (details of the methods are explained in Section
66
11th International Society for Music Information Retrieval Conference (ISMIR 2010)
5. 5.2). Then, each subject is asked to apply a five-ranked
rating for each slideshow, based on the following evalua-
tion measures:
a) Accordance between lyrics and images [content]
b) Appropriateness of image display time [duration]
c) Unity of all images in slideshow [unity]
d) Overall quality [quality]
In this experiment, we use 10 Japanese pop songs and
28 ~ 29 subjects have provided evaluation results for each
song. In order to evaluate the method described in Section
4.3, we have selected songs so that half of these songs in-
clude “combined” lyric lines (hereafter referred to as the
“combined set”) in the process of adjustment of image
transition timing explained in Section 4.3, and the other
half include “divided” lines (hereafter referred to as the
“divided set”).
5.2 Evaluated Methods
The next three methods were evaluated and compared.
A) MusicStory [9]
The first comparative method generates slideshows
based on the method proposed for MusicStory [9]. Name-
ly, all nouns are extracted from the entire lyrics of the in-
put song, and are sent to the Flickr API under the ‘OR’
combination. The images in the search result are dis-
played according to the transition timing determined by
the BPM (beats per minute) of the input song
B) TF*IDF based method
The second comparative method extracts query words
from the lyrics based on TF*IDF. The process flow to ob-
tain the image for the i-th line in the lyrics is described as
follows. First, the nouns extracted from the i-th line in the
lyrics, are sent to Flickr as query under the condition of
‘AND’ combination. If the result has no images, the noun
with the smallest TF*IDF is removed, and the rest of the
nouns are sent to Flickr again. This process is repeated
until a set of images are obtained. If the system is unable
to retrieve images by any of the nouns in the line, the im-
ages from the previous line are re-used. Finally, the high-
est-ranked image in the search result (according to the
Flickr “interestingness” ranking) is selected. Images are
obtained for each line and switched in synchronization
with line appearance within input song. In this paper, the
DF element of TF*IDF is calculated based on our data-
base, which contains 3062 Japanese pop songs.
C) Proposed method
The third method is our proposal. Queries are generat-
ed from the lyrics by the social tag-based method, images
are selected from the image search results based on the
impression score, and the image transition timing is ad-
justed by the method described in Section 4.3.
5.3 Experimental Results
Figure 3 shows the average rating of all subjects, for each
evaluation measure and method. The results in this Figure
show that the proposed method has received the highest
ratings, compared to the other methods for all evaluation
measures. Most significantly, the proposed method has
received the best rating for the overall quality, a differ-
ence which is statistically significant to the others based
on t-test (p<0.001). These results prove that the proposed
method is capable of generating high-quality slideshows.
1
2
3
4
5
content duration unity quality
Average
Evaluation Measure
MusicStory TF*IDF PROPOSAL
Figure 3. Average ratings of each evaluation measure.
Lyrics
line
“If this separation means departure, I will
give my all smiles to you.”
Query
TF*IDF based “departure” (line word)
Proposal “smile” (line word)
Lyrics
line
“Will the memory of our encounter and the
town we’d walked in be kept in our heart?”
Query
TF*IDF based “heart” (line word)
Proposal “town” (line word)
Lyrics
line
“I wish I could stay with you for even a
moment.”
Query
TF*IDF based “moment” (line word)
Proposal
“car” and “night scene”
(paragraph words)
Lyrics
line
“But the shining days will never return to
me today or tomorrow.”
Query
TF*IDF based “today” (line word)
Proposal
“evening”
(general impression word)
Table 2. Examples of image search queries generated by
TF*IDF based and proposed methods.
In order to analyze the query selection process of the
proposed method, we compare the queries generated by
the proposal to those of the TF*IDF based method. Ex-
amples are written in Table 2. This table shows examples
of lyrics lines (English translations by the authors from
the original Japanese lyrics) and the queries generated
from the lines by the two methods. In the first two exam-
ples in this table, it is clear that the proposal has success-
67
11th International Society for Music Information Retrieval Conference (ISMIR 2010)
6. fully selected words which represent visual concepts.
Contrarily, the TF*IDF method has selected words which
are important, but also are difficult to be represented in a
visual manner. This is due to the characteristic of the pro-
posed method, which considers the UF values of the
words in Flickr. Furthermore, when there are no “visual”
words in the lyrics, the proposal can appropriately gener-
ate queries, either from the lyric paragraph, or general
impression words, as shown in the last two examples.
These examples indicate that the proposed method is ef-
fective to generate good queries from any song lyric.
Moreover, even when the queries generated by the
both methods are the same, the proposal is capable of se-
lecting more suitable images for the song. For example,
when both methods retrieve images by the query “town”
for a winter song, the proposal appropriately selects an
image of a town with falling snow, while the TF*IDF
based method selects a general image of a town. Exam-
ples like this indicate that the proposed image selection
method based on impression score can generate suitable
slideshows which represent the overall theme of the song.
Additionally, in the “duration” measure, the proposal
has achieved ratings superior to the TF*IDF based me-
thod for 9 songs, indicating that the proposed adjustment
method has succeeded in improving slideshow quality.
The difference of the average ratings between the propos-
al and the TF*IDF based method for “combined sets” is
0.21, while the difference for “divided sets” is 0.61. This
result implies that the proposed method is more effective
to improve slideshows for songs with lyrics that are slow-
ly sung, as in slow ballads.
6. CONCLUSIONS AND FUTURE WORK
In this paper, we have proposed a system to generate sli-
deshows for any given song, by using words in their lyrics
to retrieve web images. We have proposed a query gener-
ation method for image search and an image selection me-
thod to compose slideshows from the image search results.
Moreover, we proposed a method to adjust image transi-
tion timing based on the lines of lyrics. Results of subjec-
tive evaluations have shown that our system can generate
highly satisfactory music slideshows.
In the future, we plan to expand our system to utilize
not only the lyrics, but also the acoustic features of the
input song. For example, displaying slideshows with vari-
ous effects, such as zooming and panning, in accordance
with the excitement of the song; as well as the use of beat
information for image transition all are expected to im-
prove the impression of the generated slideshows.
7. REFERENCES
[1] S. Iwamiya: “The interaction between auditory and
visual processing when listening to music via audio-
visual media,” The Journal of the Acoustical Society
of Japan, Vol.48, No.3, pp.146-153, 1992. [in
Japanese]
[2] R. Mayer, R. Neumayer, and A. Rauber: “Rhyme
and Style Features for Musical Genre Classification
by Song Lyrics,” Proceedings of ISMIR 2008, pp.
337-342, 2008.
[3] F. Kleedorfer, P. Knees, and T. Pohle: “Oh Oh Oh
Whoah! Towards Automatic Topic Detection in
Song Lyrics,” Proceedings of ISMIR 2008, pp. 287-
292, 2008.
[4] Y. Hu, X. Chen, and D. Yang: “Lyric-based Song
Emotion Detection with Affective Lexicon and
Fuzzy Clustering Method,” Proceedings of ISMIR
2009, pp. 123-128, 2009.
[5] X. Hu, J. S. Downie, and A. F. Ehmann: “Lyric Text
Mining in Music Mood Classification,” Proceedings
of ISMIR 2009, pp. 411-416, 2009.
[6] X. -S. Hua, L. Lu, and H. -J. Zhang: “P-Karaoke:
Personalized Karaoke System,” Proceedings of the
12th Annual ACM International Conference on
Multimedia, pp.172-173, 2004.
[7] T. Terada, M. Tsukamoto, and S. Nishino: “A
System for Presenting Background Scenes of
Karaoke Using an Active Database System,”
Proceedings of the ISCA 18th International
Conference on Computers and Their Applications,
pp. 160-165, 2003.
[8] S. Xu, T. Jin, and F. C. M. Lau: “Automatic
Generation of Music Slide Show using Personal
Photos,” Proceedings of 10th IEEE International
Symposium on Multimedia, pp. 214-219, 2008.
[9] D. A. Shamma, B. Pardo, and K. J. Hammond:
“MusicStory: a Personalized Music Video Creator,”
Proceedings of the 13th Annual ACM International
Conference on Multimedia, pp.563-566, 2005.
[10] R. Cai, L. Zhang, F. Jing, W. Lai, and W. -Y. Ma,:
“Automated Music Video Generation Using Web
Image Resource,” Proceedings of IEEE
International Conference on Acoustics, Speech, and
Signal Processing, 2007, Vol.2, pp. 737-740, 2007.
[11] Flickr: http://www.flickr.com/
[12] C. Cortes and V. Vapnik: “Support Vector
Networks,” Machine Learning, Vol. 20, pp.273-297,
1995.
[13] SVM-Light Support Vector Machine:
http://svmlight.joachims.org/
68
11th International Society for Music Information Retrieval Conference (ISMIR 2010)