Automatic Genre Classification of Musical Signals (original) (raw)

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.

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.

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.

Statistical Analysis of Features Used in Automatic Audio Genre Classification

Journal of Communication and Information Systems, 2006

This paper presents statistical models for some of the most important features used to classify audio signals into musical genres. The genres used here are selected according to a given taxonomy. The features are computed for each genre using the signals from a dataset and the results are grouped into histograms. Each proposed statistical model consists of an estimated Probability Density Function (PDF), optimized to best fit a determined histogram and the optimization criterion is the minimization of the Mean Square Error (MSE). Finally, the paper discusses how these models can be applied to classify audio signals into genres.

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 ...

Waveform-Based Musical Genre Classification

For a human, recognizing the genre of a piece of music is usually an effortless and thoughtless task; for a computer, genre classification is not a simple task. Previous research on this topic has found it to be a difficult machine learning problem. We have carefully chosen relevant features and an appropriate classification algorithm which achieve high accuracy genre classification. Features are extracted via spectral and time domain analysis, and then the LogitBoost algorithm is used to build an effective classifier for the data. This paper discusses the final feature set, why we chose those features, our final classification algorithm, and why we chose it.

A FEATURE SELECTION APPROACH FOR AUTOMATIC MUSIC GENRE CLASSIFICATION

International Journal of Semantic Computing, 2009

In this paper we present an analysis of the suitability of four different feature sets which are currently employed to represent music signals in the context of the automatic music genre classification. To such an aim, feature selection is carried out through genetic algorithms, and it is applied to multiple feature vectors generated from different segments of the music signal. The feature sets used in this paper, which encompass time-domain and frequency-domain characteristics of the music signal, comprise: short-time Fourier transform, Mel frequency cepstral coefficient, beat-related features, pitch-related features, inter-onset interval histogram coefficients, rhythm histograms and statistical spectrum descriptors. The classification is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end parts of the music signal (time-decomposition). Despite music genre classification being a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged to produce the final music genre label (space decomposition). Experiments were carried out on two databases: the Latin Music Database, which contains 3,227 music pieces categorized into ten musical genres; the ISMIR'2004 genre contest database which contains 1,458 music pieces categorized into six popular western musical genres. The experimental results have shown that the feature sets have different importance according to the part of the music signal from where the feature vectors are extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases. For high-dimensional feature sets, the feature selection provides a compact but discriminative feature subset which has an interesting trade-off between classification accuracy and computational effort.

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.

Feature Selection in Automatic Music Genre Classification

2008 Tenth IEEE International Symposium on Multimedia, 2008

This paper presents the results of the application of a feature selection procedure to an automatic music genre classification system. The classification system is based on the use of multiple feature vectors and an ensemble approach, according to time and space decomposition strategies. Feature vectors are extracted from music segments from the beginning, middle and end of the original music signal (timedecomposition). Despite being music genre classification a multi-class problem, we accomplish the task using a combination of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). As individual classifiers several machine learning algorithms were employed: Naïve-Bayes, Decision Trees, Support Vector Machines and Multi-Layer Perceptron Neural Nets. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,227 music pieces categorized in 10 musical genres. The experimental results show that the employed features have different importance according to the part of the music signal from where the feature vectors were extracted. Furthermore, the ensemble approach provides better results than the individual segments in most cases.

Instrument Independent Musical Genre Classification Using Random 3000 ms Segment

Artificial Intelligence and Neural Networks, 2006

The Turkish Artificial Intelligence and Neural Network Symposium (TAINN) is an annual meeting where scientists present their new ideas and algorithms on artificial intelligence and neural networks with either oral or poster presentation. The TAINN-Turkish Conference on AI and NN Series started in 1992 at Bilkent University in Ankara, envisioned by various researchers in AI and NN then at the Bilkent, Middle East Technical, Boğaziçi and Ege universities as a forum for local researchers to get together and communicate. Since then, TAINN has been held annually around early summer. This year the 14th TAINN conference was organized by the EE and CE departments of the İzmir Institute of Technology with an emphasis on international contributions.