A novel framework for recognizing phonemes of singing voice in polyphonic music

Hiromasa Fujihara; Masataka Goto; Hiroshi G. Okuno

doi:10.1109/ASPAA.2009.5346497

A novel framework for recognizing phonemes of singing voice in polyphonic music

Hiromasa Fujihara, Masataka Goto, Hiroshi G. Okuno

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

A novel method is described that can be used to recognize the phoneme of a singing voice (vocal) in polyphonic music. Though we focus on the voiced phoneme in this paper, this method is design to concurrently recognize other elements of a singing voice such as fundamental frequency and singer. Thus, this method is considered to be a new framework for recognizing a singing voice in polyphonic music. Our method stochastically models a mixture of a singing voice and other instrumental sounds without segregating the singing voice. It can also estimate a reliable spectral envelope by estimating it from many harmonic structures with various fundamental frequencies (F0s). The results of phoneme recognition experiments with 10 popular-music songs by 6 singers showed that our method improves the recognition accuracy by 8.7 points and achieves a 20.0% decrease in error rate.

Original language	English
Title of host publication	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Pages	17-20
Number of pages	4
DOIs	https://doi.org/10.1109/ASPAA.2009.5346497
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009 - New Paltz, NY Duration: 2009 Oct 18 → 2009 Oct 21

Other

Other	2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009
City	New Paltz, NY
Period	09/10/18 → 09/10/21

Keywords

Mixture of experts
Phoneme recognition
Singing voice
Spectral modeling

ASJC Scopus subject areas

Electrical and Electronic Engineering
Computer Science Applications

Access to Document

10.1109/ASPAA.2009.5346497

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Cite this

Fujihara, H, Goto, M & Okuno, HG 2009, A novel framework for recognizing phonemes of singing voice in polyphonic music. in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics., 5346497, pp. 17-20, 2009 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2009, New Paltz, NY, 09/10/18. https://doi.org/10.1109/ASPAA.2009.5346497

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abstract = "A novel method is described that can be used to recognize the phoneme of a singing voice (vocal) in polyphonic music. Though we focus on the voiced phoneme in this paper, this method is design to concurrently recognize other elements of a singing voice such as fundamental frequency and singer. Thus, this method is considered to be a new framework for recognizing a singing voice in polyphonic music. Our method stochastically models a mixture of a singing voice and other instrumental sounds without segregating the singing voice. It can also estimate a reliable spectral envelope by estimating it from many harmonic structures with various fundamental frequencies (F0s). The results of phoneme recognition experiments with 10 popular-music songs by 6 singers showed that our method improves the recognition accuracy by 8.7 points and achieves a 20.0% decrease in error rate.",

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AB - A novel method is described that can be used to recognize the phoneme of a singing voice (vocal) in polyphonic music. Though we focus on the voiced phoneme in this paper, this method is design to concurrently recognize other elements of a singing voice such as fundamental frequency and singer. Thus, this method is considered to be a new framework for recognizing a singing voice in polyphonic music. Our method stochastically models a mixture of a singing voice and other instrumental sounds without segregating the singing voice. It can also estimate a reliable spectral envelope by estimating it from many harmonic structures with various fundamental frequencies (F0s). The results of phoneme recognition experiments with 10 popular-music songs by 6 singers showed that our method improves the recognition accuracy by 8.7 points and achieves a 20.0% decrease in error rate.

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