Short-term Feature Space and Music Genre Classification

In music genre classification, most approaches rely on statistical characteristics of low-level features computed on short audio frames. In these methods, it is implicitly considered that frames carry equally relevant information loads and that either individual frames, or distributions thereof, somehow capture the specificities of each genre. In this paper we study the representation space defined by short-term audio features with respect to class boundaries, and compare different processing techniques to partition this space. These partitions are evaluated in terms of accuracy on two genre classification tasks, with several types of classifiers. Experiments show that a randomized and unsupervised partition of the space, used in conjunction with a Markov Model classifier lead to accuracies comparable to the state of the art. We also show that unsupervised partitions of the space tend to create less hubs.

Unsupervised automatic music genre classification

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Exploring different approaches for music genre classification

In this letter, we present different approaches for music genre classification. The proposed techniques, which are composed of a feature extraction stage followed by a classification procedure, explore both the variations of parameters used as input and the classifier architecture. Tests were carried out with three styles of music, namely blues, classical, and lounge, which are considered informally by some musicians as being “big dividers” among music genres, showing the efficacy of the proposed algorithms and establishing a relationship between the relevance of each set of parameters for each music style and each classifier. In contrast to other works, entropies and fractal dimensions are the features adopted for the classifications.

New trends in musical genre classification using optimum-path forest

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.

Towards a classification scheme for musical sounds

Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013

A multimodal genre recognition prototype

In this paper, a multimodal and interactive prototype to perform music genre classification is presented. The system is oriented to multi-part files in symbolic format but it can be adapted using a transcription system to transform audio content in music scores. This prototype uses different sources of information to give a possible answer to the user. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Os processos criativos na aprendizagem da técnica do violoncelo no ensino vocacional de música

Relatório de estágio de mestrado em Ensino de Música

Oi! Oi! Oi! : class locality and British punk

This article looks at the controversial music genre Oi! in relation to youth cultural identity in late 1970s’ and early 1980s’ Britain. As a form of British punk associated with skinheads, Oi! has oft-been dismissed as racist and bound up in the politics of the far right. It is argued here, however, that such a reading is too simplistic and ignores the more complex politics contained both within Oi! and the various youth cultural currents that revolved around the term ‘punk’ at this time. Taking as its starting point the Centre for Contemporary Cultural Studies’ conception of youth culture as a site of potential ‘resistance’, the article explores the substance and motifs of Oi!’s protest to locate its actual and perceived meaning within a far wider political and socio-economic context. More broadly, it seeks to demonstrate the value of historians examining youth culture as a formative and contested socio-cultural space within which young people discover, comprehend, and express their desires, opinions, and disaffections.

'Hey little rich boy, take a good look at me': punk, class and British oi!

This article looks at the controversial music genre Oi! in relation to youth cultural identity in late 1970s and early 1980s Britain. By examining the six compilation albums released to promote Oi! as a distinct strand of punk, it seeks to challenge prevailing dismissals of the genre as inherently racist or bound to the politics of the far right. Rather, Oi! – like punk more generally – was a contested cultural form. It was, moreover, centred primarily on questions of class and locality. To this end, Oi! sought to realize the working-class rebellion of punk’s early aesthetic; to give substance to its street-level pretentions and offer a genuine ‘song from the streets’.

A Ciropedia de Xenofonte: um romance de formação na Antiguidade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Agora eu fiquei doce: o discurso da autoestima no sertanejo universitário

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Agora eu fiquei doce: o discurso da autoestima no sertanejo universitário

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Processamento e análise de vídeos utilizando Floresta de Caminhos Ótimos

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)