Algorithmic clustering of music

@article{Cilibrasi2004AlgorithmicCO,
  title={Algorithmic clustering of music},
  author={Rudi L. Cilibrasi and Paul M. B. Vit{\'a}nyi and Ronald de Wolf},
  journal={Proceedings of the Fourth International Conference onWeb Delivering of Music, 2004. EDELMUSIC 2004.},
  year={2004},
  pages={110-117}
}
We present a method for hierarchical music clustering, based on compression of strings that represent the music pieces. The method uses no background knowledge about music whatsoever: it is completely general and can, without change, be used in different areas like linguistic classification, literature, and genomics. Indeed, it can be used to simultaneously cluster objects from completely different domains, like with like. It is based on an ideal theory of the information content in individual… 
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References

SHOWING 1-10 OF 33 REFERENCES
Folk Music Classification Using Hidden Markov Models
TLDR
The work on the classification of folk music from different countries based on their monophonic melodies using hidden Markov models suggests to us a new way to think about musical style similarity.
Music style and author-ship categorization by informative compressors
Recently, a novel parameter based on the compressibility of an informative sequence was introduced. The best compression rate of a data sequence is related to the self-similarity of the sequence and…
CLASSIFYING MUSIC BY GENRE USING THE WAVELET PACKET TRANSFORM AND A ROUND-ROBIN ENSEMBLE
TLDR
This paper presents a process for determining the music genre of an item using the Discrete Wavelet Transform and a round-robin classification technique that achieves very high classification accuracy.
Musical genre classification of audio signals
TLDR
The automatic classification of audio signals into an hierarchy of musical genres is explored and three feature sets for representing timbral texture, rhythmic content and pitch content are proposed.
Using Machine-Learning Methods for Musical Style Modeling
TLDR
This research seeks to capture some of the regularity apparent in the composition process by using statistical and information theoretic tools to analyze musical pieces and generate new works that imitate the style of the great masters.
Query by humming: musical information retrieval in an audio database
TLDR
A system for querying an audio database by humming is described along with a scheme for representing the melodic information in a song as relative pitch changes, and the performance results of system indicating its effectiveness are presented.
A Machine Learning Approach to Musical Style Recognition
TLDR
This work demonstrates that machine learning can be used to build effective style classifiers for interactive performance systems and presents an analysis explaining why these techniques work so well when hand-coded approaches have consistently failed.
Language trees and zipping.
TLDR
A very general method for extracting information from a generic string of characters, e.g., a text, a DNA sequence, or a time series based on data-compression techniques, featuring highly accurate results for language recognition, authorship attribution, and language classification.
A Block-sorting Lossless Data Compression Algorithm
TLDR
A block-sorting, lossless data compression algorithm, and the implementation of that algorithm and the performance of the implementation with widely available data compressors running on the same hardware are compared.
Algorithm makes tongue tree
TLDR
Researchers in Italy have developed a program that can spot enough subtle differences between two authors' works to attribute authorship 1 and help a computer tell Dante from Machiavelli.
...
...