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 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.
This document summarizes recent approaches in opinion mining and sentiment analysis (OMSA) in online social networks. It discusses 15 different frameworks and methods that have been proposed, including the Twitter Opinion Mining framework, an election prediction model using user influence factors, and a fuzzy deep belief network approach. The document analyzes the key steps and characteristics of each approach, such as data collection, preprocessing, classification techniques, and performance results. Overall, the paper reviews the state-of-the-art in OMSA research and highlights areas for future improvements.
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 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.
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.
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.
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.
Mood Sensitive Music Recommendation SystemIRJET Journal
The document describes a mood-sensitive music recommendation system that uses facial expression analysis to determine a user's mood and recommend matching music. It analyzes the user's facial expressions in real-time using a webcam to infer their emotional state. The system then selects music that corresponds to the user's mood based on attributes like tempo and genre. For example, if the user seems sad, it may recommend slower, melancholic music, and if happy, more upbeat music. The goal is to provide a personalized listening experience and potentially improve the user's mood. The system could be applied in music streaming or retail environments.
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.
This document summarizes recent approaches in opinion mining and sentiment analysis (OMSA) in online social networks. It discusses 15 different frameworks and methods that have been proposed, including the Twitter Opinion Mining framework, an election prediction model using user influence factors, and a fuzzy deep belief network approach. The document analyzes the key steps and characteristics of each approach, such as data collection, preprocessing, classification techniques, and performance results. Overall, the paper reviews the state-of-the-art in OMSA research and highlights areas for future improvements.
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 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.
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.
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.
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.
Mood Sensitive Music Recommendation SystemIRJET Journal
The document describes a mood-sensitive music recommendation system that uses facial expression analysis to determine a user's mood and recommend matching music. It analyzes the user's facial expressions in real-time using a webcam to infer their emotional state. The system then selects music that corresponds to the user's mood based on attributes like tempo and genre. For example, if the user seems sad, it may recommend slower, melancholic music, and if happy, more upbeat music. The goal is to provide a personalized listening experience and potentially improve the user's mood. The system could be applied in music streaming or retail environments.
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
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.
SMART MUSIC PLAYER BASED ON EMOTION DETECTIONIRJET Journal
The document describes a proposed smart music player system that performs real-time emotion detection from a user's facial expressions using their webcam. It analyzes the user's image, predicts their emotional expression, and suggests songs suitable to their detected mood. The system aims to address the problem that existing music players require users to manually browse and select songs that match their current mood. By automatically detecting the user's emotion and recommending appropriate songs, the smart music player reduces the user's workload and helps regulate their mood through music selection. The document reviews related work on facial expression-based emotion recognition and music recommendation technologies.
Deep Learning Based Music Recommendation SystemIRJET Journal
This document discusses a deep learning based music recommendation system that recommends music to users based on their analyzed mood and health parameters like heart rate and sleep patterns. It first extracts health data and analyzes a user's emotion as happy, sad, angry or neutral. Music is categorized by emotion in clusters. The system then recommends music from the cluster matching the user's detected emotion to improve their mood. It uses collaborative filtering to classify users by emotion and content-based filtering to search music matching their health inputs and analyzed emotion. The goal is to provide more personalized recommendations by considering a user's real-time emotional state.
IRJET- Implementing Musical Instrument Recognition using CNN and SVMIRJET Journal
This document summarizes research on implementing musical instrument recognition using convolutional neural networks (CNNs) and support vector machines (SVMs). The researchers aim to preprocess audio excerpts into images and use CNNs to achieve high accuracy in instrument classification. They will then combine CNN and SVM classifications and take a weighted average to achieve even higher accuracy. The document reviews several related works that used features like MFCCs and classifiers like SVMs, GMMs, and neural networks for instrument recognition. The researchers intend to use mel spectrograms and MFCCs to represent audio as images for CNN classification and improve music information retrieval and organization.
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.
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.
Music Genre Classification using Machine LearningIRJET Journal
This document discusses music genre classification using machine learning. It examines prior research that has used various machine learning algorithms like deep neural networks, CNNs, and SVMs for music genre classification. It then describes the methodology used, which includes extracting features from the GTZAN dataset, feature selection using random forest importance, training classifiers like SVM, decision tree, logistic regression, etc. on the dataset split into train and test sets. SVM with an RBF kernel performed best with 74% accuracy. Precision, recall, F1-score and support are also reported for each genre using the best model. The summaries show the dataset, methods, and key results of evaluating different machine learning models for music genre classification.
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.
IRJET- Musical Instrument Recognition using CNN and SVMIRJET Journal
This document discusses a study that uses convolutional neural networks (CNNs) and support vector machines (SVMs) to recognize musical instruments in audio recordings. The researchers aim to convert audio excerpts to images and use CNNs to classify instruments, then combine the CNN classifications with SVM classifications to improve accuracy. They discuss related work on instrument recognition using other methods. The proposed model uses MFCC features with SVM and passes audio converted to images through four convolutional layers and fully connected layers in the CNN. Combining the CNN and SVM results through weighted averaging is expected to provide higher accuracy than either method alone for classifying instruments in the IRMAS dataset.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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.
CONTENT BASED AUDIO CLASSIFIER & FEATURE EXTRACTION USING ANN TECNIQUESAM Publications
Audio signals which include speech, music and environmental sounds are important types of media. The problem of distinguishing audio signals into these different audio types is thus becoming increasingly significant. A human listener can easily distinguish between different audio types by just listening to a short segment of an audio signal. However, solving this problem using computers has proven to be very difficult. Nevertheless, many systems with modest accuracy could still be implemented. The experimental results demonstrate the effectiveness of our classification system. The complete system is developed in ANN Techniques with Autonomic Computing system
Music information retrieval system through qbshSravani Rebba
The document describes a proposed music information retrieval system that uses both melody and lyric information to improve accuracy over existing query-by-singing/humming systems that only use melody. The system can distinguish between singing and humming queries and applies different techniques - for humming it uses melody recognition while for singing it computes lyric similarity to combine with melody distance for retrieval. The goal is to reduce error rates by exploiting both melody and lyric clues.
Optimized audio classification and segmentation algorithm by using ensemble m...Venkat Projects
The document proposes an optimized audio classification and segmentation algorithm that segments audio streams into four types - pure speech, music, environment sound, and silence - using ensemble methods. It uses a hybrid classification approach of bagged support vector machines and artificial neural networks. The algorithm aims to accurately segment audio with minimum misclassification and requires less training data, making it suitable for real-time applications. It segments non-speech portions into music or environment sound and further divides speech into silence and pure speech. The algorithm achieves approximately 98% accurate segmentation.
Literature Survey for Music Genre Classification Using Neural NetworkIRJET Journal
The document discusses literature on classifying music genres using neural networks. It summarizes several past studies that used techniques like convolutional neural networks (CNNs) and mel-frequency cepstral coefficients (MFCCs) on datasets like GTZAN to classify music into genres like blues, classical, country, etc. The document also outlines the system design for a proposed music genre classification system, including collecting the GTZAN dataset, preprocessing the audio files into mel-spectrograms, extracting features using MFCCs, and training a CNN model to classify segments of songs into genres. Classification accuracy of different models from prior studies ranged from 40-80%.
Computational Approaches for Melodic Description in Indian Art Music CorporaSankalp Gulati
Presentation for my PhD defense, Music Technology Group, Barcelona, Spain.
Resources: http://compmusic.upf.edu/node/304
Short abstract:
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify melodic patterns, compare them both within and across music pieces, and finally, characterize the specific melodic context of IAM, the rāgas. All these analyses are done using data-driven methodologies on sizable curated music corpora. Our work paves the way for addressing several interesting research problems in the field of music information research, as well as developing novel applications in the context of music discovery and music pedagogy.
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.
Automatic Music Generation Using Deep LearningIRJET Journal
This document discusses automatic music generation using deep learning. It begins with an abstract describing how music is generated in the form of a sequence of ABC notes using deep learning concepts. LSTM or GRUs are commonly used for music generation as recurrent neural networks that can efficiently model sequences. The main purpose of the project described is to generate melodious and rhythmic music automatically using a recurrent neural network. It reviews approaches like WaveNet and LSTM for music generation and tools like Magenta and DeepJazz. The design uses a character RNN and LSTM network to classify and predict the next character in an ABC notation sequence to generate music.
IRJET- Machine Learning and Noise Reduction Techniques for Music Genre Classi...IRJET Journal
This document discusses using machine learning and deep learning techniques to classify music genres automatically. It proposes applying noise reduction techniques to audio files using Fourier analysis before feeding them into models. A convolutional neural network is trained on mel-spectrograms of audio to classify genres. Supervised machine learning models like random forest and XGBoost are also explored using extracted audio features. The proposed system applies noise reduction to preprocessed audio then uses a CNN or supervised learning models to classify music genres.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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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.
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The document proposes an optimized audio classification and segmentation algorithm that segments audio streams into four types - pure speech, music, environment sound, and silence - using ensemble methods. It uses a hybrid classification approach of bagged support vector machines and artificial neural networks. The algorithm aims to accurately segment audio with minimum misclassification and requires less training data, making it suitable for real-time applications. It segments non-speech portions into music or environment sound and further divides speech into silence and pure speech. The algorithm achieves approximately 98% accurate segmentation.
Literature Survey for Music Genre Classification Using Neural NetworkIRJET Journal
The document discusses literature on classifying music genres using neural networks. It summarizes several past studies that used techniques like convolutional neural networks (CNNs) and mel-frequency cepstral coefficients (MFCCs) on datasets like GTZAN to classify music into genres like blues, classical, country, etc. The document also outlines the system design for a proposed music genre classification system, including collecting the GTZAN dataset, preprocessing the audio files into mel-spectrograms, extracting features using MFCCs, and training a CNN model to classify segments of songs into genres. Classification accuracy of different models from prior studies ranged from 40-80%.
Computational Approaches for Melodic Description in Indian Art Music CorporaSankalp Gulati
Presentation for my PhD defense, Music Technology Group, Barcelona, Spain.
Resources: http://compmusic.upf.edu/node/304
Short abstract:
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify melodic patterns, compare them both within and across music pieces, and finally, characterize the specific melodic context of IAM, the rāgas. All these analyses are done using data-driven methodologies on sizable curated music corpora. Our work paves the way for addressing several interesting research problems in the field of music information research, as well as developing novel applications in the context of music discovery and music pedagogy.
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.
Automatic Music Generation Using Deep LearningIRJET Journal
This document discusses automatic music generation using deep learning. It begins with an abstract describing how music is generated in the form of a sequence of ABC notes using deep learning concepts. LSTM or GRUs are commonly used for music generation as recurrent neural networks that can efficiently model sequences. The main purpose of the project described is to generate melodious and rhythmic music automatically using a recurrent neural network. It reviews approaches like WaveNet and LSTM for music generation and tools like Magenta and DeepJazz. The design uses a character RNN and LSTM network to classify and predict the next character in an ABC notation sequence to generate music.
IRJET- Machine Learning and Noise Reduction Techniques for Music Genre Classi...IRJET Journal
This document discusses using machine learning and deep learning techniques to classify music genres automatically. It proposes applying noise reduction techniques to audio files using Fourier analysis before feeding them into models. A convolutional neural network is trained on mel-spectrograms of audio to classify genres. Supervised machine learning models like random forest and XGBoost are also explored using extracted audio features. The proposed system applies noise reduction to preprocessed audio then uses a CNN or supervised learning models to classify music genres.
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TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
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STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
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A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
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P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
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Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
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Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
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Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
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politics, and conventional and nontraditional security are all explored and explained by the researcher.
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in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
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International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
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Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Understanding Inductive Bias in Machine LearningSUTEJAS
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The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
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dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
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Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.