Algorithmic Clustering of Music Based on String Compression

Cilibrasi, Rudi; Vitányi, Paul M. B.; Wolf, Ronald de

doi:10.1162/0148926042728449

Cited by 179 publications

(125 citation statements)

References 9 publications

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“…Since the amount of digitized music has increased enormously over the past years, the need for tools to organize, order and cluster music has increased as well. Automatic music classification has already been investigated on the basis of many different ideas, among others compression distance [2], Hidden Markov Models [1], stochastic language models [12], and n-gram models [5].…”

Section: Introductionmentioning

confidence: 99%

Clustering and Classification of Music by Interval Categories

Honingh

Bod

2011

Mathematics and Computation in Music

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Abstract. In this paper, we present a novel approach to clustering and classification of music. The approach is based on the concept of interval categories, which is a theoretical, but perceptually motivated construct that groups sets of pitch events into six different categories each of which has its own musical character. A piece of music can be represented by six percentages that express the occurrences of each category in the piece. This piece of music can, in this way, be represented as one point in a six dimensional space. We choose the three most significant dimensions to visualize music from different composers and genres. We will see that, using this approach, automatic classification between tonal and atonal music is possible. Furthermore, we will present a successful visual clustering of 1) the three periods of Beethoven, 2) the genres Rock and Jazz, and 3) several composers through various musical time periods.

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Section: Introductionmentioning

confidence: 99%

Clustering and Classification of Music by Interval Categories

Honingh

Bod

2011

Mathematics and Computation in Music

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“…One useful application of AIT to real-world phenomena has been through the Universal Similarity Metric and its uses in genetics and bioinformatics [31,34,20,26], plagiarism detection [11] and even analysis of music [13]. In [12], the authors combine a restricted variant of Kolmogorov complexity with results obtained from Google searches to derive a metric for the similarity of the meaning of words and phrases.…”

Section: Algorithmic Information Theorymentioning

confidence: 99%

Impugning Randomness, Convincingly

Gurevich

Passmore

2012

Stud Logica

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John organized a state lottery and his wife won the main prize. You may feel that the event of her winning wasn't particularly random, but how would you argue that in a fair court of law? Traditional probability theory does not even have the notion of random events. Algorithmic information theory does, but it is not applicable to real-world scenarios like the lottery one. We attempt to rectify that.

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“…Complexity has also been shown to be related to liking [10] in such a way that we tend to like the music the most that have medium complexity -too simple and too complex music is less preferred. Also, music complexity has been used for clustering music in genres [2,11,5], and in [4], a measure of Information Rate computed over a piece of music was shown to correlate in significant ways with familiarity ratings and emotional force response profiles by human subjects.…”

Section: Music Complexity As a Measure Of Interestingnessmentioning

confidence: 99%

Automatic Reduction of MIDI Files Preserving Relevant Musical Content

Madsen

Typke

Widmer

2010

Lecture Notes in Computer Science

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Abstract. Retrieving music from large digital databases is a demanding computational task. The cost for indexing and searching depends not only on the computational effort of measuring musical similarity, but also heavily on the number and sizes of files in the database. One way to speed up music retrieval is to reduce the search space by removing redundant and uninteresting material in the database. We propose a simple measure of 'interestingness' based on music complexity, and present a reduction algorithm for MIDI files based on this measure. It is evaluated by comparing reduction ratios and the correctness of retrieval results for a query by humming task before and after applying the reduction.

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Algorithmic Clustering of Music Based on String Compression

Cited by 179 publications

References 9 publications

Clustering and Classification of Music by Interval Categories

Clustering and Classification of Music by Interval Categories

Impugning Randomness, Convincingly

Automatic Reduction of MIDI Files Preserving Relevant Musical Content

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