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Application of text-based methods of analysis to symbolic music

Author: Jacek Wołkowicz; Dalhousie University. Faculty of Computer Science,
Publisher: Halifax, NS : Dalhousie University, 2013. ©2013.
Dissertation: Thesis Ph.D. Dalhousie University, 2013.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
ABSTRACT: This dissertation features methods of analyzing symbolic music, focused on n-gram-based approaches, as this representation resembles the most text and natural languages. The analysis of similarities between several text and music corpora is accompanied with implementation of text-based methods for problems of composer classification and symbolic music similarity definition. Both problems contain thorough  Read more...
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Details

Genre/Form: Electronic thesis or dissertation
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jacek Wołkowicz; Dalhousie University. Faculty of Computer Science,
OCLC Number: 946568384
Notes: Title from PDF title page (viewed Apr. 12, 2016).
Thesis supervisor: Vlado Keselj.
Description: 1 online resource (xii, 238 leaves) : illustrations (some colour).
Responsibility: by Jacek Wołkowicz.

Abstract:

ABSTRACT: This dissertation features methods of analyzing symbolic music, focused on n-gram-based approaches, as this representation resembles the most text and natural languages. The analysis of similarities between several text and music corpora is accompanied with implementation of text-based methods for problems of composer classification and symbolic music similarity definition. Both problems contain thorough evaluation of performance of the systems with comparisons to other approaches on existing testbeds. It is also described how one can use this symbolic representation in conjunction with genetic algorithms to tackle problems like melody generation. The proposed method is fully automated, and the process utilizes n-gram statistics from a sample corpus to achieve it. A method of visualization of complex symbolic music pieces is also presented. It consist of creating a self similarity matrix of a piece in question, revealing dependencies between voices, themes and sections, as well as music structure. A fully automatic technique of inferring music structure from these similarity matrices is also presented The proposed structure analysis system is compared against similar approaches that operate on audio data. The evaluation shows that the presented structure analysis system outperformed significantly all audio-based algorithms available for comparison in both precision and recall.

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