The Rhythm Transform: Towards A Generic Rhythm Description (original) (raw)

Classification Into Musical Genres Using A Rhythmic Kernel

Proceedings of the SMC Conferences, 2004

Beginning with the question on how to determine the genre of a music piece, we elaborate on the representation of rhythm for the classification into genres. The aim of such classification differs in principle from that of traditional Music Informa tion Retrieval algorithms. First, we formalise the rhythmic representation of music fragments. This formalism is then used to construct a similarity function called kernel. To allow the discrete comparison of rhythmic fragments, a pre-processing step in th e algorithm computes a common quantization unit among the input data. A simple injective mapping into N allows the kernel to employ the Euclidean dot product. A small database of jazz, classical and rock fragments is used in an implementation of a Support Vector Machine. The issues that arise with different time signatures are analysed. Finally, we share some early results of the experiments comparing the three genres, showing that rhythm conveys good information for classification, within the conditions of the experiment.

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.

Audio genre classification using percussive pattern clustering combined with timbral features

2009

Abstract Many musical genres and styles are characterized by distinct representative rhythmic patterns. In most automatic genre classification systems global statistical features based on timbral dynamics such as mel-frequency cepstral coefficients (MFCC) are utilized but so far rhythmic information has not so effectively been used. In order to extract bar-long unit rhythmic patterns for a music collection we propose a clustering method based on one-pass dynamic programming and k-means clustering.

Automatic genre classification using large high-level musical feature sets

2004

This paper presents an automatic description system of drum sounds for real-world musical audio signals. Our system can represent onset times and names of drums by means of drum descriptors defined in the context of MPEG-7. For their automatic description, drum sounds must be identified in such polyphonic signals. The problem is that acoustic features of drum sounds vary with each musical piece and precise templates for them cannot be prepared in advance. To solve this problem, we propose new template-adaptation and template-matching methods. The former method adapts a single seed template prepared for each kind of drums to the corresponding drum sound appearing in an actual musical piece. The latter method then can detect all the onsets of each drum by using the corresponding adapted template. The onsets of bass and snare drums in any piece can thus be identified. Experimental results showed that the accuracy of identifying bass and snare drums in popular music was about 90%. Finally, we define drum descriptors in the MPEG-7 format and demonstrate an example of the automatic drum sound description for a piece of popular music.

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 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.

Representing Musical Genre: A State of the Art

Journal of New Music Research, 2003

Musical genre is probably the most popular music descriptor. In the context of large musical databases and Electronic Music Distribution, genre is therefore a crucial metadata for the description of music content. However, genre is intrinsically ill-defined and attempts at defining genre precisely have a strong tendency to end up in circular, ungrounded projections of fantasies. Is genre an intrinsic attribute of music titles, as, say, tempo? Or is genre a extrinsic description of the whole piece? In this article, we discuss the various approaches in representing musical genre, and propose to classify these approaches in three main categories: manual, prescriptive and emergent approaches. We discuss the pros and cons of each approach, and illustrate our study with results of the Cuidado IST project.

Comparing Timbre-based Features for Musical Genre Classification

People can accurately classify music based on its style by listening to less than half a second of audio. This has motivated efforts to build accurate predictive models of musical genre based upon short-time musical descriptions. In this context, perceptually relevant features have been considered crucial but only little research has been con- ducted in this direction. This study compared two tim- bral features for supervised classification of musical gen- res: 1) the Mel-Frequency Cepstral Coefficients (MFCC), coming from the speech domain and widely used for mu- sic modeling purposes; and 2) the more recent Sub-Band Flux (SBF) set of features which has been designed specif- ically for modeling human perception of polyphonic mu- sical timbre. Differences in performance between models were found, suggesting that the SBF feature set is more ap- propriate for musical genre classification than the MFCC set. In addition, spectral fluctuations at both ends of the frequency spectrum were found to be relevant for discrim- ination between musical genres. The results of this study give support to the use of perceptually motivated features for musical genre classification.

Symbolic musical genre classification based on repeating patterns

Proceedings of the 1st ACM workshop on Audio and music computing multimedia - AMCMM '06, 2006

This paper presents a genre classification algorithm for symbolic music data. The proposed methodology relies on note pitch and duration features, derived from the repeating patterns and duration histograms of a musical piece, respectively. Note-information histograms have a great capability in capturing a fair amount of information regarding harmonic as well as rhythmic features of different musical genres and pieces, while repeating patterns refer to segments of the piece that are semantically important. Detailed experimental results on intra-classical genres illustrate the significant performance gains due to the proposed features.

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.