Different amplitude and time distribution of the sound of light and classical music

Diodati, P.; Piazza, S.

doi:10.1007/s100510070170

Cited by 5 publications

(6 citation statements)

References 8 publications

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“…pop and techno) are located closer to the fully aleatory limit (white noise). In this context, our approach agrees with other works [23,24,28].…”

Section: Data Presentation and Resultssupporting

confidence: 92%

“…Correa et al [25] investigated four music genres employing a complex network representation for rhythmic features of the songs. There are still other investigations [26][27][28][29][30][31][32][33][34][35][36][37][38][39], most of which are based on fractal dimensions, entropies, power spectrum analysis or correlation analysis. It is worth noting that there are several methods of automatic genre classification emerging from engineering disciplines (see, for instance, Ref.…”

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confidence: 99%

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Complexity–entropy causality plane: A useful approach for distinguishing songs

Ribeiro

Zunino

Mendes

et al. 2012

Physica A: Statistical Mechanics and its Applications

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Nowadays we are often faced with huge databases resulting from the rapid growth of data storage technologies. This is particularly true when dealing with music databases. In this context, it is essential to have techniques and tools able to discriminate properties from these massive sets. In this work, we report on a statistical analysis of more than ten thousand songs aiming to obtain a complexity hierarchy. Our approach is based on the estimation of the permutation entropy combined with an intensive complexity measure, building up the complexity-entropy causality plane. The results obtained indicate that this representation space is very promising to discriminate songs as well as to allow a relative quantitative comparison among songs. Additionally, we believe that the here-reported method may be applied in practical situations since it is simple, robust and has a fast numerical implementation.

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“…pop and techno) are located closer to the fully aleatory limit (white noise). In this context, our approach agrees with other works [23,24,28].…”

Section: Data Presentation and Resultssupporting

confidence: 92%

mentioning

confidence: 99%

Complexity–entropy causality plane: A useful approach for distinguishing songs

Ribeiro

Zunino

Mendes

et al. 2012

Physica A: Statistical Mechanics and its Applications

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“…In order to construct this rank for music genres, we evaluate the mean value of c over all songs of each music genre consid- ered here as shown in Figure 3a. Our findings agree with other works in the sense that there is a quantitative difference between classic and light/dancing music [33,34]. However, it is interesting to emphasize that music genres are not a well defined concept [38].…”

supporting

confidence: 91%

“…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 times and sound amplitudes larger than a fixed value.…”

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confidence: 66%

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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“…Inspired by such studies that analyze correlations in recurrence time of large events, in the present manuscript the same idea has been applied to music. P. Diodati and S. Piazza [16] had done a similar study with a focus on Gioacchino Rossinis La calunnia 'e un venticello from Il barbiere di Siviglia. We probe the origin of the correlations by systematically varying the musical parameters.…”

Section: Introductionmentioning

confidence: 99%

Analysis of music: controlled random music and probability distribution function of recurrence time of amplitude peaks

Sreekumar,

Verma,

Narain

et al. 2008

Preprint

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Correlations in music that exist within its waveform are studied. Monophonic wave files of random music are generated and the probability distribution function of time interval between large signal values is analyzed. A power law behavior for the distribution function in the range from 0.1millisecond to 20 milliseconds is observed. An attempt is made to investigate the origin of these correlations by randomizing each of the factors (frequencies, intensities and durations of notes of the random music files) separately.

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Different amplitude and time distribution of the sound of light and classical music

Cited by 5 publications

References 8 publications

Complexity–entropy causality plane: A useful approach for distinguishing songs

Complexity–entropy causality plane: A useful approach for distinguishing songs

Universal patterns in sound amplitudes of songs and music genres

Analysis of music: controlled random music and probability distribution function of recurrence time of amplitude peaks

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