Musical genres: beating to the rhythms of different drums

Corrêa, Débora C.; Saito, José Hiroki; Costa, Luciano da Fontoura

doi:10.1088/1367-2630/12/5/053030

Cited by 18 publications

(24 citation statements)

References 32 publications

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“…Despite the complex situation that emerges in the problem of automatic genre classification [41][42][43][44], our model is very simple. From the qualitative point of view, the characteristic of songs and music genres is related with multidimensional aspects like timbre, melody, harmony, rhythm, among others.…”

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confidence: 99%

Universal patterns in sound amplitudes of songs and music genres

et al. 2011

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We report a statistical analysis over more than eight thousand songs. Specifically, we investigate the probability distribution of the normalized sound amplitudes. Our findings seems to suggest a universal form of distribution which presents a good agreement with a one-parameter stretched Gaussian. We also argue that this parameter can give information on music complexity, and consequently it goes towards classifying songs as well as music genres. Additionally, we present statistical evidences that correlation aspects of the songs are directly related with the non-Gaussian nature of their sound amplitude distributions. In recent years, studies of complex systems have become widespread among the scientific community, specially in the statistical physics one [1][2][3][4][5]. Many of these investigations deal with data records ordered in time or space (i.e., time series), trying to extract some features, patterns or laws that may be present in the systems studied. This approach has been successfully applied to a variety of fields, from physics and astronomy [6] Music is a well known worldwide social phenomenon linked to the human cognitive habits, modes of consciousness as well as historical developments [14]. In the direction of music's social role, some authors investigated collective listening habits. For instance, Lambiotte and Ausloos [15] analyzed data from people music library finding audience groups with the size distribution following a power law. They also investigated correlations among these music groups, reporting non-trivial relations [16]. In another work, Silva et al.[17] studied the network structure of the song writers and the singers of Brazilian popular music (mpb). There is also an interest in the behavior of music sales [18] as well as in the success of musicians [19][20][21].Despite these cultural aspects, songs form a highly organized system presenting very complex structures and long-range correlations. All these features have attracted the attention of statistical physicists. In a seminal paper, Voss and Clarke [22] analyzed the power spectrum of radio stations and observed a 1/f noise like pattern. They also showed that the correlation can extend to longer or shorter time scales, depending on the music genre. Hsü and Hsü[23] investigated the changes of acoustic frequency in Bach's and Mozart's compositions, finding self-similarity and fractals structures. In contrast, they report no resemblance to fractal geometry [24] for modern music. Fractal structures have also been reported in the study of sequences of music notes [25], where Su and Wu [26] suggest that the multifractal spectrum can be used to distinguish different styles of music. In this brief literature review, we see that special attention was paid to the fractal structures of music, correlations and power spectrum analysis. However, much less attention has been paid to the understanding of the amplitude distribution. This last point has been noted by Diodati and Piazza[34]. In their work, they investigated the distribution of t...

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Universal patterns in sound amplitudes of songs and music genres

et al. 2011

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“…The process of dilatation (with the radius parameter r = 10) results in 85 features of volume and 84 features of area. After that they are concatenated (totaling 169 features) and the supervised classification is carried out by applying a Canonical Analysis [18] followed by a Linear Discriminant Analysis (LDA) (also called Fisher linear discriminant) [27]. The Canonical Analysis is employed here due the presence of high correlation of the features (specially volume features).…”

Section: Applications On Imagesmentioning

confidence: 99%

“…In this way, this work proposes to enhance the Bouligand-Minkowski descriptors by including information from the boundary, that is, the area in a three-dimensional space. The combination is accomplished by means of a simple concatenation of measures, followed by a dimensionality reduction through the canonical analysis [18]. The performance of the proposal is assessed over databases of texture images and the results are compared to other classical and state-of-the-art methods in the literature.…”

Section: Introductionmentioning

confidence: 99%

Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation

Oliveira

Casanova

Florindo

et al. 2014

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• A new fractal descriptor that combines Minkowski dilatation boundary and interior is proposed.• Minkowski dilatation boundary and interior carry out different pattern information.• Results show it is capable to increase the pattern recognition power of the traditional approach. t r a c tThis work proposes to obtain novel fractal descriptors from gray-level texture images by combining information from interior and boundary measures of the Minkowski dilation applied to the texture surface. At first, the image is converted into a surface where the height of each point is the gray intensity of the respective pixel in that position in the image. Thus, this surface is morphologically dilated by spheres. The radius of such spheres is ranged within an interval and the volume and the external area of the dilated structure are computed for each radius. The final descriptors are given by such measures concatenated and subject to a canonical transform to reduce the dimensionality. The proposal is an enhancement to the classical Bouligand-Minkowski fractal descriptors, where only the volume (interior) information is considered. As different structures may have the same volume, but not the same area, the proposal yields to more rich descriptors as confirmed by results on the classification of benchmark databases.

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“…identificação vegetal por análise foliar). Nesse sentido optamos por utilizar a análise discriminante linear, ou LDA (do inglês linear discriminant analysis), em conjunto com um classificador bayesiano e k-vizinhos mais próximo KNN (do inglês k-nearest neighbors), para avaliar a qualidade dos métodos testados (202). A validação cruzada 10-foldé empregada para avaliar a capacidade de generalização dos modelos.…”

Section: Reconhecimento De Padrõesunclassified

Redes complexas em visão computacional com aplicações em bioinformática

Casanova¹

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AGRADECIMENTOSAos meus pais Darci e Acioní, que mesmoà distância, me apoiam e me encorajaram em todos os momentos.A minha amada namorada Larissa, pela felicidade a mim proporcionada, pela compreensão, carinho e companhia oferecida durante o tempo em que me mantive distante.Ao meu orientador Prof. Dr. Odemir Martinez Bruno, pela confiança depositada em mim, orientação e amizade.Aos amigos que fiz no convívio diário da universidade, e aos amigos do ciclismo, que me proporcionaram viagens, diversão e experiênciasúnicas.Aos professores e funcionários do IFSC -USP e a todos que, direta ou indiretamente, colaboraram comigo.A FAPESP pelo apoio financeiro.Eu não tenhoídolos. Tenho admiração por trabalho, dedicação e competência. Complex networks is a relatively recent field of study, that has called the attention of the scientific community and has been successfully applied in different areas such as computer networking, sociology, medicine, physics, mathematics and others. However the literature shows that there are few works that employ complex networks in feature extraction of images for later analysis or classification. Given an image, it can be modeled as a network, extract topological features and, using these measures, build the classifier desired. This work aims, therefore, investigate this type of application, analyzing new forms of modeling an image as a complex network and investigate some topological features to characterize images. In order to analyze the potential of the techniques developed, we selected a major challenge in the field of computer vision: plant identification by leaf analysis. The plant identification is an important task in many research fields such as biodiversity, ecology, botany, pharmacology and others.

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Musical genres: beating to the rhythms of different drums

Cited by 18 publications

References 32 publications

Universal patterns in sound amplitudes of songs and music genres

Universal patterns in sound amplitudes of songs and music genres

Enhancing fractal descriptors on images by combining boundary and interior of Minkowski dilation

Redes complexas em visão computacional com aplicações em bioinformática

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