Waveform-Based Musical Genre Classification (original) (raw)
Related papers
Automatic Musical Genre Classification of Audio Signals
Proceedings of the 2nd International Symposium on …, 2001
Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.
Musical genre classification of audio signals
Speech and Audio Processing, IEEE …, 2002
Musical genres are categorical labels created by humans to characterize pieces of music. A musical genre is characterized by the common characteristics shared by its members. These characteristics typically are related to the instrumentation, rhythmic structure, and harmonic content of the music. Genre hierarchies are commonly used to structure the large collections of music available on the Web. Currently musical genre annotation is performed manually. Automatic musical genre classification can assist or replace the human user in this process and would be a valuable addition to music information retrieval systems. In addition, automatic musical genre classification provides a framework for developing and evaluating features for any type of content-based analysis of musical signals.
Proceedings of the International Computer Music …, 2004
This paper examines the potential of high-level features extracted from symbolic musical representations in regards to musical classification. Twenty features are implemented and tested by using them to classify 225 MIDI files by genre. This system differs from previous automatic genre classification systems, which have focused on low-level features extracted from audio data. Files are classified into three parent genres and nine sub-genres, with average success rates of 84.8% for the former and 57.8% for the latter. Classification is performed by a novel configuration of feed-forward neural networks that independently classify files by parent genre and sub-genre and combine the results using weighted averages.
Factors in automatic musical genre classification of audio signals
2003
Abstract Automatic musical genre classification is an important tool for organizing the large collections of music that are becoming available to the average user. In addition, it provides a structured way of evaluating musical content features that does not require extensive user studies. The paper provides a detailed comparative analysis of various factors affecting automatic classification performance, such as choice of features and classifiers.
Automatic genre classification using large high-level musical feature sets
2004
This paper presents a system that extracts 109 musical features from symbolic recordings (MIDI, in this case) and uses them to classify the recordings by genre. The features used here are based on instrumentation, texture, rhythm, dynamics, pitch statistics, melody and chords. The classification is performed hierarchically using different sets of features at different levels of the hierarchy. Which features are used at each level, and their relative weightings, are determined using genetic algorithms. Classification is performed using a novel ensemble of feedforward neural networks and k-nearest neighbour classifiers. Arguments are presented emphasizing the importance of using high-level musical features, something that has been largely neglected in automatic classification systems to date in favour of low-level features. The effect on classification performance of varying the number of candidate features is examined in order to empirically demonstrate the importance of using a large variety of musically meaningful features. Two differently sized hierarchies are used in order to test the performance of the system under different conditions. Very encouraging classification success rates of 98% for root genres and 90% for leaf genres are obtained for a hierarchical taxonomy consisting of 9 leaf genres.
Automatic Genre Classification of Musical Signals
IV Congress of Brazilian Audio Engineering Society, 2006
Apresentado no 4 o Congresso da AES Brasil 10 a Convenção Nacional da AES Brasil 08 a 10 de Maio de 2006, São Paulo, SP Este artigo foi reproduzido do original final entregue pelo autor, sem edições, correções ou considerações feitas pelo comitê técnico. A AES Brasil não se responsabiliza pelo conteúdo. Outros artigos podem ser adquiridos através da Audio Engineering Society, .aes.org. Informações sobre a seção Brasileira podem ser obtidas em www.aesbrasil.org. Todos os direitos são reservados. Não é permitida a reprodução total ou parcial deste artigo sem autorização expressa da AES Brasil.
A Machine Learning Approach to Automatic Music Genre Classification
Journal of The Brazilian Computer Society, 2008
This paper presents a non-conventional approach for the automatic music genre classification problem. The proposed approach uses multiple feature vectors and a pattern recognition ensemble approach, according to space and time decomposition schemes. Despite being music genre classification a multi-class problem, we accomplish the task using a set of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). Music segments are also decomposed according to time segments obtained from the beginning, middle and end parts of the original music signal (time-decomposition). The final classification is obtained from the set of individual results, according to a combination procedure. Classical machine learning algorithms such as Naïve-Bayes, Decision Trees, k Nearest-Neighbors, Support Vector Machines and MultiLayer Perceptron Neural Nets are employed. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,160 music pieces categorized in 10 musical genres. Experimental results show that the proposed ensemble approach produces better results than the ones obtained from global and individual segment classifiers in most cases. Some experiments related to feature selection were also conducted, using the genetic algorithm paradigm. They show that the most important features for the classification task vary according to their origin in the music signal.
A hierarchical approach to automatic musical genre classification
Proc. 6th Int. Conf. on Digital Audio Effects’ 03, 2003
A system for the automatic classification of audio signals according to audio category is presented. The signals are recognized as speech, background noise and one of 13 musical genres. A large number of audio features are evaluated for their suitability in such a classification task, including well-known physical and perceptual features, audio descriptors defined in the MPEG-7 standard, as well as new features proposed in this work. These are selected with regard to their ability to distinguish between a given set of audio ...
Automatic music classification into genres
Musical genres are categorical labels created by humans to characterize pieces of music. Although music genres are inexact and can often be quite arbitrary and controversial, it is believed that certain song characteristics like instrumentation, rhythmic structure, and harmonic content of the music are related to the genre. In this paper, the task of automatic music genre classification is explored. Multiple features based on timbral texture, rhythmic content and pitch content are extracted from a single music piece and used to train different classifiers for genre prediction. The experiments were performed using features extracted from one or two 30 second segments from each song. For the classification, two different architectures flat and hierarchical classification and three different classifiers (kNN, MLP and SVM) were tried. The experiments were performed on the full feature set (316 features) and on a PCA reduced feature set. The testing speed of the classifiers was also measured.The experiments carried out on a large dataset containing more than 1700 music samples from ten different music genres have shown accuracy of 69.1% for the flat classification architecture (utilizing one against all SVM based classifiers). The accuracy obtained using the hierarchical classification architecture was slightly lower 68.8%, but four times faster than the flat architecture.