Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations

<p/> <p>One major goal of structural analysis of an audio recording is to automatically extract the repetitive structure or, more generally, the musical form of the underlying piece of music. Recent approaches to this problem work well for music, where the repetitions largely agree with...

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Main Authors: M&#252;ller Meinard, Kurth Frank
Format: Article
Language:English
Published: SpringerOpen 2007-01-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://asp.eurasipjournals.com/content/2007/089686
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spelling doaj-3876674f7ffb4e45b47e4b6898745be92020-11-24T22:03:12ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802007-01-0120071089686Towards Structural Analysis of Audio Recordings in the Presence of Musical VariationsM&#252;ller MeinardKurth Frank<p/> <p>One major goal of structural analysis of an audio recording is to automatically extract the repetitive structure or, more generally, the musical form of the underlying piece of music. Recent approaches to this problem work well for music, where the repetitions largely agree with respect to instrumentation and tempo, as is typically the case for popular music. For other classes of music such as Western classical music, however, musically similar audio segments may exhibit significant variations in parameters such as dynamics, timbre, execution of note groups, modulation, articulation, and tempo progression. In this paper, we propose a robust and efficient algorithm for audio structure analysis, which allows to identify musically similar segments even in the presence of large variations in these parameters. To account for such variations, our main idea is to incorporate invariance at various levels simultaneously: we design a new type of statistical features to absorb microvariations, introduce an enhanced local distance measure to account for local variations, and describe a new strategy for structure extraction that can cope with the global variations. Our experimental results with classical and popular music show that our algorithm performs successfully even in the presence of significant musical variations.</p> http://asp.eurasipjournals.com/content/2007/089686
collection DOAJ
language English
format Article
sources DOAJ
author M&#252;ller Meinard
Kurth Frank
spellingShingle M&#252;ller Meinard
Kurth Frank
Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations
EURASIP Journal on Advances in Signal Processing
author_facet M&#252;ller Meinard
Kurth Frank
author_sort M&#252;ller Meinard
title Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations
title_short Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations
title_full Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations
title_fullStr Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations
title_full_unstemmed Towards Structural Analysis of Audio Recordings in the Presence of Musical Variations
title_sort towards structural analysis of audio recordings in the presence of musical variations
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2007-01-01
description <p/> <p>One major goal of structural analysis of an audio recording is to automatically extract the repetitive structure or, more generally, the musical form of the underlying piece of music. Recent approaches to this problem work well for music, where the repetitions largely agree with respect to instrumentation and tempo, as is typically the case for popular music. For other classes of music such as Western classical music, however, musically similar audio segments may exhibit significant variations in parameters such as dynamics, timbre, execution of note groups, modulation, articulation, and tempo progression. In this paper, we propose a robust and efficient algorithm for audio structure analysis, which allows to identify musically similar segments even in the presence of large variations in these parameters. To account for such variations, our main idea is to incorporate invariance at various levels simultaneously: we design a new type of statistical features to absorb microvariations, introduce an enhanced local distance measure to account for local variations, and describe a new strategy for structure extraction that can cope with the global variations. Our experimental results with classical and popular music show that our algorithm performs successfully even in the presence of significant musical variations.</p>
url http://asp.eurasipjournals.com/content/2007/089686
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