Music Recommendation Application (Team Neurobytes)
•
0 likes•11 views
Overview of CMPE258 Project
Report
Share
Report
Share
Download to read offline
Recommended
web based music genre classification.pptx
In the above ppt we will have keen explanation about web based music genre classifications based on the clips of 10 seconds
music recommendation system ,Based on Million Song Dataset
Music Recommendation System is a cutting-edge technology that uses algorithms to analyze user preferences and behavior in order to suggest personalized music recommendations.
A music recommendation system project is a fascinating endeavor that combines elements of data analysis, machine learning, and user experience design to create personalized music suggestions for users. Here's a breakdown of what such a project might entail:
Data Collection: The first step is gathering data. This could include information about songs, albums, artists, genres, user preferences, listening history, and more. APIs from music streaming platforms like Spotify or Last.fm are often used to access this data.
Data Preprocessing: Once the data is collected, it needs to be cleaned and preprocessed. This involves tasks like removing duplicates, handling missing values, and converting data into a suitable format for analysis.
Feature Engineering: Features are attributes or characteristics of the data that can be used to make predictions or recommendations. In the context of music recommendation, features could include song tempo, genre, artist popularity, user listening history, and so on. Feature engineering involves selecting and creating relevant features from the data.
Model Selection: There are various machine learning algorithms that can be used for recommendation systems, such as collaborative filtering, content-based filtering, matrix factorization, and deep learning models. The choice of model depends on factors like the type of data available and the specific requirements of the project.
Training the Model: Once a model is selected, it needs to be trained on the preprocessed data. During training, the model learns patterns and relationships in the data that enable it to make accurate recommendations.
Evaluation: After training the model, it's important to evaluate its performance. This involves using metrics like precision, recall, and accuracy to assess how well the model predicts user preferences and provides relevant recommendations.
Deployment: Once the model is trained and evaluated, it can be deployed into a production environment where users can interact with it. This might involve integrating the recommendation system into a music streaming platform or building a standalone application.
Feedback Loop: A good recommendation system should be able to adapt to changing user preferences over time. This requires implementing a feedback loop where user interactions with the system are continuously monitored and used to update the model and improve the recommendations.
Throughout the development process, it's important to consider factors like scalability, usability, and privacy to ensure that the recommendation system is both effective and user-friendly. Additionally, incorporating techniques like A/B testing can help optimize the system and refine its recommendations based on real-world user feedback.
Music Recommendation System using Euclidean, Cosine Similarity, Correlation D...
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.
IRJET- Implementation of Emotion based Music Recommendation System using SVM ...
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.
Resume_Sung-Yen_Liu
This document contains contact information and a summary of the qualifications and experience of Sung-Yen (Sean) Liu. It outlines his education including a Master's degree from Columbia University and a Bachelor's degree from National Taiwan University. It also details his professional experience conducting research in music signal processing and machine learning at Academia Sinica in Taiwan. Some of his project experience includes implementing classification algorithms for handwritten digits recognition and developing privacy-protected chat room and music database applications while at Columbia University.
Developing A Semantic Web Application - ISWC 2008 tutorial
The document describes the development of a semantic web application called Music Event Explorer (meex) that will integrate data from multiple existing music-related data sources using semantic web technologies. It will allow users to explore music events related to artists and styles. The application will merge data about artists, music styles, and events from sources like MusicBrainz, MusicMoz, and EVDB into a unified RDF model using tools like RDF, OWL, and SPARQL. The development will follow good software engineering practices for a semantic web application.
Btp 3rd Report
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.
IRJET- Analysis of Music Recommendation System using Machine Learning Alg...
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.
Recommended
web based music genre classification.pptx
In the above ppt we will have keen explanation about web based music genre classifications based on the clips of 10 seconds
music recommendation system ,Based on Million Song Dataset
Music Recommendation System is a cutting-edge technology that uses algorithms to analyze user preferences and behavior in order to suggest personalized music recommendations.
A music recommendation system project is a fascinating endeavor that combines elements of data analysis, machine learning, and user experience design to create personalized music suggestions for users. Here's a breakdown of what such a project might entail:
Data Collection: The first step is gathering data. This could include information about songs, albums, artists, genres, user preferences, listening history, and more. APIs from music streaming platforms like Spotify or Last.fm are often used to access this data.
Data Preprocessing: Once the data is collected, it needs to be cleaned and preprocessed. This involves tasks like removing duplicates, handling missing values, and converting data into a suitable format for analysis.
Feature Engineering: Features are attributes or characteristics of the data that can be used to make predictions or recommendations. In the context of music recommendation, features could include song tempo, genre, artist popularity, user listening history, and so on. Feature engineering involves selecting and creating relevant features from the data.
Model Selection: There are various machine learning algorithms that can be used for recommendation systems, such as collaborative filtering, content-based filtering, matrix factorization, and deep learning models. The choice of model depends on factors like the type of data available and the specific requirements of the project.
Training the Model: Once a model is selected, it needs to be trained on the preprocessed data. During training, the model learns patterns and relationships in the data that enable it to make accurate recommendations.
Evaluation: After training the model, it's important to evaluate its performance. This involves using metrics like precision, recall, and accuracy to assess how well the model predicts user preferences and provides relevant recommendations.
Deployment: Once the model is trained and evaluated, it can be deployed into a production environment where users can interact with it. This might involve integrating the recommendation system into a music streaming platform or building a standalone application.
Feedback Loop: A good recommendation system should be able to adapt to changing user preferences over time. This requires implementing a feedback loop where user interactions with the system are continuously monitored and used to update the model and improve the recommendations.
Throughout the development process, it's important to consider factors like scalability, usability, and privacy to ensure that the recommendation system is both effective and user-friendly. Additionally, incorporating techniques like A/B testing can help optimize the system and refine its recommendations based on real-world user feedback.
Music Recommendation System using Euclidean, Cosine Similarity, Correlation D...
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.
IRJET- Implementation of Emotion based Music Recommendation System using SVM ...
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.
Resume_Sung-Yen_Liu
This document contains contact information and a summary of the qualifications and experience of Sung-Yen (Sean) Liu. It outlines his education including a Master's degree from Columbia University and a Bachelor's degree from National Taiwan University. It also details his professional experience conducting research in music signal processing and machine learning at Academia Sinica in Taiwan. Some of his project experience includes implementing classification algorithms for handwritten digits recognition and developing privacy-protected chat room and music database applications while at Columbia University.
Developing A Semantic Web Application - ISWC 2008 tutorial
The document describes the development of a semantic web application called Music Event Explorer (meex) that will integrate data from multiple existing music-related data sources using semantic web technologies. It will allow users to explore music events related to artists and styles. The application will merge data about artists, music styles, and events from sources like MusicBrainz, MusicMoz, and EVDB into a unified RDF model using tools like RDF, OWL, and SPARQL. The development will follow good software engineering practices for a semantic web application.
Btp 3rd Report
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.
IRJET- Analysis of Music Recommendation System using Machine Learning Alg...
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.
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
About
The document summarizes a proof-of-concept song recommendation engine that uses data from HypeMachine, including 125,566 songs and 900k user-song interactions from 9,000 users over the past year. It generates recommendations using a two-stage collaborative filtering approach: 1) calculating song commonalities between users to generate features, and 2) using those features in a machine learning model to predict whether a user will like a song based on their past likes. The model achieved 78% accuracy in predicting if a user liked a song and 64% predictive accuracy based on AUC.
Song Proffer
The document summarizes a proof-of-concept song recommendation engine that uses data from HypeMachine, including 125,566 songs and 900k user-song interactions from 9,000 users over the past year. It generates recommendations using a two-stage collaborative filtering approach: 1) calculating song commonalities between users to generate features, and 2) using those features in a machine learning model to predict whether a user will like a song based on historical like data. The model achieved 78% accuracy in predicting if a user liked a song and 64% predictive accuracy based on AUC.
Treever pitchdeck
The document describes a mobile application called Treever that allows users to create and share custom multimedia messages called "treeves". Key features include integrating users' photos, music and text to automatically generate unique 8-15 second "digital dopamine delights"; scheduling and automation tools; and potential revenue streams like subscriptions, branded templates, and partnerships. It provides an overview of the company's vision to bring greater emotional engagement to mobile messaging through music and discusses their platform, development roadmap, and financial needs.
Mehfil : Song Recommendation System Using Sentiment Detected
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
Ampd (Technical Report)
Ampd is a proposed music streaming platform that allows users to upload, listen to, and discuss music. It aims to give smaller artists exposure by focusing on hosting a wide range of music rather than just popular songs. The platform would allow live streaming of new music from artists, as well as social media integration and data analysis features to recommend similar songs and provide analytics to help artists. The proposed software would be built using Node.js for the backend server, React for the frontend client, and MongoDB for the database. Key features would include the ability to upload and stream songs, an online chat for discussing music, and analytics dashboards for artists.
Twente ir-course 20-10-2010
Search logs from user interactions with image archives can be analyzed and utilized in three ways:
1. To understand user search behavior and how professional users search differently than average users.
2. As training data to automatically annotate images with concepts using similar queries and clicked images, though reliability varies by concept.
3. As additional positive training samples to improve automated image classification systems, especially when combined with manually annotated samples.
Emofy
This document provides an overview of a dissertation on Emofy, a classical music recommender system. The summary includes:
- Emofy is a music recommender system that recommends classical Indian music based on the user's mood by classifying moods and associating different ragas and genres with different moods.
- The dissertation discusses collecting and labeling a dataset of classical music, extracting features to classify mood, and using machine learning algorithms like random forests to achieve over 90% accuracy in mood classification.
- The recommended system uses mood classification to map users to appropriate ragas and playlists of classical music tracks on Spotify aimed at therapeutic applications.
Abstract For PhD-Dissertation The Use Of Music Files At The Intersection Bet...
This dissertation examines how young music listeners, creators, and distributors manage music files at the intersection of downloading and streaming practices. Through interviews with 16 music listeners, 10 professional musicians, and 4 distributors from Spotify, TDC Play, Wimp, and 24-7 Entertainment, the study analyzes the technical and sociocultural aspects of digital music use. The research finds that music files are understood and handled as both downloads and streams through various platforms and software. While distributors optimize user experience, musicians remain skeptical of streaming formats' potential. The study suggests music use is culturally translated through computer-oriented ideologies as files are remediated between download, stream, and other formats.
survey on Hybrid recommendation mechanism to get effective ranking results fo...
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.
Francesco Serafin
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
Advancing concatenative sound synthesis technology
The document discusses advances in concatenative sound synthesis technology. It proposes a concatenative sound synthesis model using queries to provide more flexibility and user options. This would minimize the need for transformation and editing of sound snippets. The concatenation results are affected by factors like the number of segments, source and target files, and segmentation mode and unit selection method. Examples show how sounds from different sources could be concatenated to create a country song. Work continues on addressing other issues in concatenative sound synthesis.
Variations2
The document summarizes the Variations2 project, which is building on an earlier Variations project funded by the National Science Foundation. Variations2 aims to create an integrated digital library of musical works, scores, and recordings. It is staffed by several librarians and supported by various Indiana University departments. The project involves developing a data model and software framework to provide search and retrieval of diverse music formats. Usability research is also being conducted to improve the user experience.
Analyzing Cross-System User Modeling on the Social Web
This document summarizes research on cross-system user modeling using data from Twitter, Flickr, and Delicious. It finds that a user's tag-based profiles on different systems overlap little, but aggregating profiles reveals more information. Cross-system user modeling significantly improves recommendation quality for new users compared to single-system or content-based approaches. The best strategies adapt to factors like the source and target systems. Overall, cross-system modeling is effective for cold-start recommendations by enriching sparse individual profiles.
Spotify Stream Prediction using Regression Models
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.
visualization-discography-analysis(2)
1) The document describes a project that uses machine learning techniques to analyze and classify songs from an artist's discography based on audio features.
2) Songs are clustered based on similarity of audio features to learn more about the artist's career and musical influences over time.
3) The best results grouped David Bowie's songs into 3 to 6 clusters but Pink Floyd's discography proved very difficult to cluster, showing variation in how well the methods worked for different artists.
Multimedia Semantics: Metadata, Analysis and Interaction
Multimedia Semantics: Metadata, Analysis and Interaction. Keynote Talk at the Latin-American Conference on Networked Electronic Media (LACNEM), August 2009, Bogota, Colombia
Tacconi PhD final exam
Final presentation of the thesis work entitled Systems and solutions for mobile pervasive computing environments
Mining User Lifecycles from Online Community Platforms and their Application ...
This document summarizes research on mining user lifecycles from online community platforms and applying the findings to churn prediction. Key points:
- User development was analyzed using properties like in-degree, out-degree, and language over equal time periods to derive lifecycle stages.
- Period entropy and cross-entropy with previous periods/community were computed to quantify variation and convergence of properties over time.
- Linear regression models were fit to lifecycle property trajectories for individual users, showing most had decreasing period entropy over time.
- The goal is to understand user development and forecast churners based on early signals, enabling recommendations and neighborhood-based systems.
Exploring Generative Models of Tripartite Graphs for Recommendation in Social...
This document discusses generative models for tripartite graphs in social media that model users, resources, and tags. It presents three models:
1) The User-Concept model that models users based on their tag usage but ignores resources and social aspects.
2) The User-Resource model that models resources as vocabulary terms but ignores tags and social aspects.
3) The User-Resource-Concept model that models both resources and users' interests using a topic-based representation and models the social generation of annotations.
The models are evaluated on their ability to predict tags/resources for new users, recommend social ties, and compare to baseline similarity metrics, with the ensemble approach achieving the best performance.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
More Related Content
Similar to Music Recommendation Application (Team Neurobytes)
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
About
The document summarizes a proof-of-concept song recommendation engine that uses data from HypeMachine, including 125,566 songs and 900k user-song interactions from 9,000 users over the past year. It generates recommendations using a two-stage collaborative filtering approach: 1) calculating song commonalities between users to generate features, and 2) using those features in a machine learning model to predict whether a user will like a song based on their past likes. The model achieved 78% accuracy in predicting if a user liked a song and 64% predictive accuracy based on AUC.
Song Proffer
The document summarizes a proof-of-concept song recommendation engine that uses data from HypeMachine, including 125,566 songs and 900k user-song interactions from 9,000 users over the past year. It generates recommendations using a two-stage collaborative filtering approach: 1) calculating song commonalities between users to generate features, and 2) using those features in a machine learning model to predict whether a user will like a song based on historical like data. The model achieved 78% accuracy in predicting if a user liked a song and 64% predictive accuracy based on AUC.
Treever pitchdeck
The document describes a mobile application called Treever that allows users to create and share custom multimedia messages called "treeves". Key features include integrating users' photos, music and text to automatically generate unique 8-15 second "digital dopamine delights"; scheduling and automation tools; and potential revenue streams like subscriptions, branded templates, and partnerships. It provides an overview of the company's vision to bring greater emotional engagement to mobile messaging through music and discusses their platform, development roadmap, and financial needs.
Mehfil : Song Recommendation System Using Sentiment Detected
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
Ampd (Technical Report)
Ampd is a proposed music streaming platform that allows users to upload, listen to, and discuss music. It aims to give smaller artists exposure by focusing on hosting a wide range of music rather than just popular songs. The platform would allow live streaming of new music from artists, as well as social media integration and data analysis features to recommend similar songs and provide analytics to help artists. The proposed software would be built using Node.js for the backend server, React for the frontend client, and MongoDB for the database. Key features would include the ability to upload and stream songs, an online chat for discussing music, and analytics dashboards for artists.
Twente ir-course 20-10-2010
Search logs from user interactions with image archives can be analyzed and utilized in three ways:
1. To understand user search behavior and how professional users search differently than average users.
2. As training data to automatically annotate images with concepts using similar queries and clicked images, though reliability varies by concept.
3. As additional positive training samples to improve automated image classification systems, especially when combined with manually annotated samples.
Emofy
This document provides an overview of a dissertation on Emofy, a classical music recommender system. The summary includes:
- Emofy is a music recommender system that recommends classical Indian music based on the user's mood by classifying moods and associating different ragas and genres with different moods.
- The dissertation discusses collecting and labeling a dataset of classical music, extracting features to classify mood, and using machine learning algorithms like random forests to achieve over 90% accuracy in mood classification.
- The recommended system uses mood classification to map users to appropriate ragas and playlists of classical music tracks on Spotify aimed at therapeutic applications.
Abstract For PhD-Dissertation The Use Of Music Files At The Intersection Bet...
This dissertation examines how young music listeners, creators, and distributors manage music files at the intersection of downloading and streaming practices. Through interviews with 16 music listeners, 10 professional musicians, and 4 distributors from Spotify, TDC Play, Wimp, and 24-7 Entertainment, the study analyzes the technical and sociocultural aspects of digital music use. The research finds that music files are understood and handled as both downloads and streams through various platforms and software. While distributors optimize user experience, musicians remain skeptical of streaming formats' potential. The study suggests music use is culturally translated through computer-oriented ideologies as files are remediated between download, stream, and other formats.
survey on Hybrid recommendation mechanism to get effective ranking results fo...
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.
Francesco Serafin
This is the presentation for his admission to the third year of his Ph.D.. It talks about the several direction his work had taken and look forward to the conclusion of some task in form of code release and published papers.
Advancing concatenative sound synthesis technology
The document discusses advances in concatenative sound synthesis technology. It proposes a concatenative sound synthesis model using queries to provide more flexibility and user options. This would minimize the need for transformation and editing of sound snippets. The concatenation results are affected by factors like the number of segments, source and target files, and segmentation mode and unit selection method. Examples show how sounds from different sources could be concatenated to create a country song. Work continues on addressing other issues in concatenative sound synthesis.
Variations2
The document summarizes the Variations2 project, which is building on an earlier Variations project funded by the National Science Foundation. Variations2 aims to create an integrated digital library of musical works, scores, and recordings. It is staffed by several librarians and supported by various Indiana University departments. The project involves developing a data model and software framework to provide search and retrieval of diverse music formats. Usability research is also being conducted to improve the user experience.
Analyzing Cross-System User Modeling on the Social Web
This document summarizes research on cross-system user modeling using data from Twitter, Flickr, and Delicious. It finds that a user's tag-based profiles on different systems overlap little, but aggregating profiles reveals more information. Cross-system user modeling significantly improves recommendation quality for new users compared to single-system or content-based approaches. The best strategies adapt to factors like the source and target systems. Overall, cross-system modeling is effective for cold-start recommendations by enriching sparse individual profiles.
Spotify Stream Prediction using Regression Models
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.
visualization-discography-analysis(2)
1) The document describes a project that uses machine learning techniques to analyze and classify songs from an artist's discography based on audio features.
2) Songs are clustered based on similarity of audio features to learn more about the artist's career and musical influences over time.
3) The best results grouped David Bowie's songs into 3 to 6 clusters but Pink Floyd's discography proved very difficult to cluster, showing variation in how well the methods worked for different artists.
Multimedia Semantics: Metadata, Analysis and Interaction
Multimedia Semantics: Metadata, Analysis and Interaction. Keynote Talk at the Latin-American Conference on Networked Electronic Media (LACNEM), August 2009, Bogota, Colombia
Tacconi PhD final exam
Final presentation of the thesis work entitled Systems and solutions for mobile pervasive computing environments
Mining User Lifecycles from Online Community Platforms and their Application ...
This document summarizes research on mining user lifecycles from online community platforms and applying the findings to churn prediction. Key points:
- User development was analyzed using properties like in-degree, out-degree, and language over equal time periods to derive lifecycle stages.
- Period entropy and cross-entropy with previous periods/community were computed to quantify variation and convergence of properties over time.
- Linear regression models were fit to lifecycle property trajectories for individual users, showing most had decreasing period entropy over time.
- The goal is to understand user development and forecast churners based on early signals, enabling recommendations and neighborhood-based systems.
Exploring Generative Models of Tripartite Graphs for Recommendation in Social...
This document discusses generative models for tripartite graphs in social media that model users, resources, and tags. It presents three models:
1) The User-Concept model that models users based on their tag usage but ignores resources and social aspects.
2) The User-Resource model that models resources as vocabulary terms but ignores tags and social aspects.
3) The User-Resource-Concept model that models both resources and users' interests using a topic-based representation and models the social generation of annotations.
The models are evaluated on their ability to predict tags/resources for new users, recommend social ties, and compare to baseline similarity metrics, with the ensemble approach achieving the best performance.
Similar to Music Recommendation Application (Team Neurobytes) (20)
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
Mehfil : Song Recommendation System Using Sentiment Detected
Mehfil : Song Recommendation System Using Sentiment Detected
Abstract For PhD-Dissertation The Use Of Music Files At The Intersection Bet...
Abstract For PhD-Dissertation The Use Of Music Files At The Intersection Bet...
survey on Hybrid recommendation mechanism to get effective ranking results fo...
survey on Hybrid recommendation mechanism to get effective ranking results fo...
Advancing concatenative sound synthesis technology
Advancing concatenative sound synthesis technology
Analyzing Cross-System User Modeling on the Social Web
Analyzing Cross-System User Modeling on the Social Web
Multimedia Semantics: Metadata, Analysis and Interaction
Multimedia Semantics: Metadata, Analysis and Interaction
Mining User Lifecycles from Online Community Platforms and their Application ...
Mining User Lifecycles from Online Community Platforms and their Application ...
Exploring Generative Models of Tripartite Graphs for Recommendation in Social...
Exploring Generative Models of Tripartite Graphs for Recommendation in Social...
Recently uploaded
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Recently uploaded (20)
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Infrastructure Challenges in Scaling RAG with Custom AI models
Infrastructure Challenges in Scaling RAG with Custom AI models
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Music Recommendation Application (Team Neurobytes)
- 1. TEAM NEUROBYTES Music Recommendation Application Implementation of Large Language Models and MLOps
- 2. Project Overview The proliferation of digital music platforms has escalated the demand for sophisticated music recommendation systems that not only understand user preferences but also appreciate the complex musicality of songs. We address this need by deploying a state-of- the-art large-language model that processes a fusion of user interaction metrics and intrinsic song features to deliver personalized music recommendations through MLOp practices.
- 3. The cloud database chosen has data related to: User-to-Song rating/playback information 1. Song-to-Song similarity information based on musicality. 2. Data DATASET 3 DATASET 2 DATASET 1 user_preferences.csv music_data.csv millionsong_dataset.csv Approach
- 4. Methods Approach Model Preprocessing & Training 01 02 03 Exploratory Analysis & Prototyping ML Production Cloud Pipline Get million song subset data song list Get metadata and join the data use artist similarity and artists to train the model on similarity Exploring track and user preference related data AWS RDBS, My SQL
- 5. Methods Approach Re-training & CI/CD 06 02 03 Evaluation with Tensorboard UI/UX Using Gradio Production of reccomnedations Evaluating Model and Accuracy Implementing model with user experience using gradio and huggingface as a tool
- 6. 1 Spotify API 2 LastFM API 3 EDA Experiments Testing data related to Spotify API Connecting User Preferences to the Million Song Dataset and LastFM API Exploratory Data Analysis of the Synthetic User Preferences
- 8. Future work TEAM NEUROBYTES We successfully produced a music recommendation system using traditional MLOp practices. For future work, we could potentially scale this larger and implement the system with existing music listening platforms. Conclusion
- 9. TEAM NEUROBYTES Thank you for listening For any questions feel free to contact our team.