System for Automatic Singing Voice Recognition - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing.
Gespeichert in:
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Zwan, Pawel [verfasserIn] |
|---|
| Format: |
Artikel |
|---|
| Erschienen: |
2008 |
|---|
| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Audio Engineering Society - New York, NY : AES, 1953, 56(2008), 9, Seite 710-723 |
|---|---|
| Übergeordnetes Werk: |
volume:56 ; year:2008 ; number:9 ; pages:710-723 ; extent:14 |
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sw081103 (DE-627)OLC1803392398 (DE-599)GBVOLC1803392398 DE-627 ger DE-627 rakwb 620 33.12 bkl 50.36 bkl 53.79 bkl Zwan, Pawel verfasserin aut System for Automatic Singing Voice Recognition - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing. 2008 14 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Kostek, Bozena oth Enthalten in Journal of the Audio Engineering Society New York, NY : AES, 1953 56(2008), 9, Seite 710-723 (DE-627)129358207 (DE-600)160393-0 (DE-576)014730421 1549-4950 nnns volume:56 year:2008 number:9 pages:710-723 extent:14 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_120 GBV_ILN_122 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_2015 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4307 GBV_ILN_4315 GBV_ILN_4316 GBV_ILN_4327 GBV_ILN_4700 33.12 AVZ 50.36 AVZ 53.79 AVZ AR 56 2008 9 710-723 14 |
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sw081103 (DE-627)OLC1803392398 (DE-599)GBVOLC1803392398 DE-627 ger DE-627 rakwb 620 33.12 bkl 50.36 bkl 53.79 bkl Zwan, Pawel verfasserin aut System for Automatic Singing Voice Recognition - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing. 2008 14 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Kostek, Bozena oth Enthalten in Journal of the Audio Engineering Society New York, NY : AES, 1953 56(2008), 9, Seite 710-723 (DE-627)129358207 (DE-600)160393-0 (DE-576)014730421 1549-4950 nnns volume:56 year:2008 number:9 pages:710-723 extent:14 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_120 GBV_ILN_122 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_2015 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4307 GBV_ILN_4315 GBV_ILN_4316 GBV_ILN_4327 GBV_ILN_4700 33.12 AVZ 50.36 AVZ 53.79 AVZ AR 56 2008 9 710-723 14 |
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sw081103 (DE-627)OLC1803392398 (DE-599)GBVOLC1803392398 DE-627 ger DE-627 rakwb 620 33.12 bkl 50.36 bkl 53.79 bkl Zwan, Pawel verfasserin aut System for Automatic Singing Voice Recognition - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing. 2008 14 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Kostek, Bozena oth Enthalten in Journal of the Audio Engineering Society New York, NY : AES, 1953 56(2008), 9, Seite 710-723 (DE-627)129358207 (DE-600)160393-0 (DE-576)014730421 1549-4950 nnns volume:56 year:2008 number:9 pages:710-723 extent:14 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_120 GBV_ILN_122 GBV_ILN_170 GBV_ILN_2004 GBV_ILN_2015 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4307 GBV_ILN_4315 GBV_ILN_4316 GBV_ILN_4327 GBV_ILN_4700 33.12 AVZ 50.36 AVZ 53.79 AVZ AR 56 2008 9 710-723 14 |
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System for Automatic Singing Voice Recognition - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing. |
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System for Automatic Singing Voice Recognition - A neural network was trained and tested to provide automated classification of singing voices, both recognizing voice quality (amateur, semiprofessional, and professional) and voice type (bass, baritone, tenor, alto, mezzo-soprano, and soprano). Parameters related to singing were defined to form feature vectors. Single vowel samples for each singer were judged by six experts to establish a quality index. In a test based on a database of 2690 samples, 90% of the decisions were correct. These results show that it is possible to use neural networks to create an expert system to evaluate singing. |
| remote_bool |
false |
| author2 |
Kostek, Bozena |
| author2Str |
Kostek, Bozena |
| ppnlink |
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| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| up_date |
2024-07-03T21:09:09.148Z |
| _version_ |
1803593658294861824 |
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