Sequential Complexity as a Descriptor for Musical Similarity

Foster, Peter; Mauch, Matthias; Dixon, Simon

doi:10.1109/taslp.2014.2357676

Cited by 8 publications

(8 citation statements)

References 49 publications

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“…Like previous studies of pop-music history [ 28 , 30 ], our study is based on features extracted from audio rather than from scores. However, where these early studies focused on technical aspects of audio such as loudness, vocabulary statistics and sequential complexity, we have attempted to identify musically meaningful features.…”

Section: Introductionmentioning

confidence: 99%

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The evolution of popular music: USA 1960–2010

Mauch

MacCallum

Lévy³

et al. 2015

R. Soc. open sci.

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170

175

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In modern societies, cultural change seems ceaseless. The flux of fashion is especially obvious for popular music. While much has been written about the origin and evolution of pop, most claims about its history are anecdotal rather than scientific in nature. To rectify this, we investigate the US Billboard Hot 100 between 1960 and 2010. Using music information retrieval and text-mining tools, we analyse the musical properties of approximately 17 000 recordings that appeared in the charts and demonstrate quantitative trends in their harmonic and timbral properties. We then use these properties to produce an audio-based classification of musical styles and study the evolution of musical diversity and disparity, testing, and rejecting, several classical theories of cultural change. Finally, we investigate whether pop musical evolution has been gradual or punctuated. We show that, although pop music has evolved continuously, it did so with particular rapidity during three stylistic ‘revolutions’ around 1964, 1983 and 1991. We conclude by discussing how our study points the way to a quantitative science of cultural change.

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Section: Introductionmentioning

confidence: 99%

“…That has changed with the emergence of large, digitized, collections of audio recordings, musical scores and lyrics. Quantitative studies of musical evolution have quickly followed [26][27][28][29][30]. Here, we use a corpus of digitized music to investigate the history of American popular music.…”

Section: Introductionmentioning

confidence: 99%

The evolution of popular music: USA 1960–2010

Mauch

MacCallum

Lévy³

et al. 2015

R. Soc. open sci.

Self Cite

170

175

View full text Add to dashboard Cite

show abstract

“…This paper proposed a solution by introducing an acceleration scheme to overcome KNN drawbacks via a combination of moment descriptors with traditional KNN. The moment descriptors have been utilized well in multimedia research for various applications, such as musical similarity and song year prediction [15], speed up color image fractal compression [16] and enhance fractal audio compression [17]. The training set will be arranged into subsets; samples belong to the same subset have similar descriptor number.…”

Section:  Issn: 1693-6930mentioning

confidence: 99%

Enhancement of student performance prediction using modified K-nearest neighbor

2020

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The traditional K-nearest neighbor (KNN) algorithm uses an exhaustive search for a complete training set to predict a single test sample. This procedure can slow down the system to consume more time for huge datasets. The selection of classes for a new sample depends on a simple majority voting system that does not reflect the various significance of different samples (i.e. ignoring the similarities among samples). It also leads to a misclassification problem due to the occurrence of a double majority class. In reference to the above-mentioned issues, this work adopts a combination of moment descriptor and KNN to optimize the sample selection. This is done based on the fact that classifying the training samples before the searching actually takes place can speed up and improve the predictive performance of the nearest neighbor. The proposed method can be called as fast KNN (FKNN). The experimental results show that the proposed FKNN method decreases original KNN consuming time within a range of (75.4%) to (90.25%), and improve the classification accuracy percentage in the range from (20%) to (36.3%) utilizing three types of student datasets to predict whether the student can pass or fail the exam automatically.

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“…Specifically two musical objects are similar to the extent that a model of one can be used to generate a compressed representation of the other. Previous research in MIR has used compression distance to classify music using symbolic representations such as MIDI (Hilleware, Manderick, & Conklin, 2012;Cataltepe, Yaslan, & Sonmez, 2007;Li & Sleep, 2004;Cilibrasi, Vitányi, & de Wolf, 2004;Meredith, 2014) and audio representations (Ahonen, 2010;Cataltepe et al, 2007;Li & Sleep, 2005;Foster, Mauch, & Dixon, 2014). Compression distance is applied to high-level musical features known to be used in cognitive representations of musical melody and the resulting system is evaluated as a cognitive model by comparing its similarity ratings with human judgements of perceived musical similarity.…”

Section: Introductionmentioning

confidence: 99%

Compression-based Modelling of Musical Similarity Perception

Pearce

Müllensiefen

2017

Journal of New Music Research

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Similarity is an important concept in music cognition research since the similarity between (parts of) musical pieces determines perception of stylistic categories and structural relationships between parts of musical works. The purpose of the present research is to develop and test models of musical similarity perception inspired by a transformational approach which conceives of similarity between two perceptual objects in terms of the complexity of the cognitive operations required to transform the representation of the first object into that of the second, a process which has been formulated in informationtheoretic terms. Specifically, computational simulations are developed based on compression distance in which a probabilistic model is trained on one piece of music and then used to predict, or compress, the notes in a second piece. The more predictable the second piece according to the model, the more efficiently it can be encoded and the greater the similarity between the two pieces. The present research extends an existing information-theoretic model of auditory expectation (IDyOM) to compute compression distances varying in symmetry and normalisation using high-level symbolic features representing aspects of pitch and rhythmic structure. Comparing these compression distances with listeners' similarity ratings between pairs of melodies collected in three experiments demonstrates that the compression-based model provides a good fit to the data and allows the identification of representations, model parameters and compression-based metrics that best account for musical similarity perception. The compression-based model also shows comparable performance to the best-performing algorithms on the MIREX 2005 melodic similarity task.

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Sequential Complexity as a Descriptor for Musical Similarity

Cited by 8 publications

References 49 publications

The evolution of popular music: USA 1960–2010

The evolution of popular music: USA 1960–2010

Enhancement of student performance prediction using modified K-nearest neighbor

Compression-based Modelling of Musical Similarity Perception

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