publication . Other literature type . Conference object . Preprint . 2017

Score-Informed Syllable Segmentation For A Cappella Singing Voice With Convolutional Neural Networks.

Pons Puig, Jordi; Gong, Rong; Serra, Xavier;
Open Access
  • Published: 23 Oct 2017
  • Publisher: Zenodo
  • Country: Spain
Abstract
Comunicació presentada a la 18th International Society for Music Information Retrieval Conference (ISMIR 2017), celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina.
Subjects
free text keywords: Música -- Informàtica, Computer Science - Sound
Funded by
EC| COMPMUSIC
Project
COMPMUSIC
Computational models for the discovery of the world's music
  • Funder: European Commission (EC)
  • Project Code: 267583
  • Funding stream: FP7 | SP2 | ERC
Download fromView all 6 versions
Zenodo
Other literature type . 2017
Provider: Datacite
ZENODO
Conference object . 2017
Provider: ZENODO
Zenodo
Other literature type . 2017
Provider: Datacite

[6] D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.

[7] A. Klapuri. Sound onset detection by applying psychoacoustic knowledge. In ICASSP, Phoenix, USA, 1999.

[8] Anna M. Kruspe. Keyword spotting in a-capella singing. In ISMIR, Taipei, Taiwan, 2014. [OpenAIRE]

[9] Emilio Molina, Ana M. Barbancho, Lorenzo J. Tardn, and Isabel Barbancho. Evaluation Framework for Automatic Singing Transcription. In ISMIR, Taipei, Taiwan, 2014. [OpenAIRE]

[10] N. Obin, F. Lamare, and A. Roebel. Syll-O-Matic: An adaptive time-frequency representation for the automatic segmentation of speech into syllables. In ICASSP, Vancouver, Canada, 2013. [OpenAIRE]

[11] Jordi Pons and Xavier Serra. Designing efficient architectures for modeling temporal features with convolutional neural networks. In ICASSP, New orleans, USA, 2017.

[12] Jordi Pons, Olga Slizovskaia, Rong Gong, Emilia Go´mez, and Xavier Serra. Timbre analysis of music audio signals with convolutional neural networks. arxiv:1703.06697, 2017. [OpenAIRE]

[13] L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286, February 1989.

[14] Rafael Caro Repetto and Xavier Serra. Creating a Corpus of Jingju (Beijing Opera) Music and Possibilities for Melodic Analysis. In ISMIR, Taipei, Taiwan, 2014. [OpenAIRE]

[15] Odette Scharenborg, Vincent Wan, and Mirjam Ernestus. Unsupervised speech segmentation: An analysis of the hypothesized phone boundaries. The Journal of the Acoustical Society of America, 127(2):1084-1095, 2010. [OpenAIRE]

[16] J. Schlu¨ter and S. Bo¨ck. Improved musical onset detection with convolutional neural networks. In ICASSP, Florence, Italy, 2014.

[17] Chee-Chuan Toh, Bingjun Zhang, and Ye Wang. Multiple-feature fusion based onset detection for solo singing voice. In ISMIR, Philadelphia, USA, 2008. [OpenAIRE]

[18] Elizabeth Wichmann. Listening to Theatre: The Aural Dimension of Beijing Opera. University of Hawaii Press, 1991.

Abstract
Comunicació presentada a la 18th International Society for Music Information Retrieval Conference (ISMIR 2017), celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina.
Subjects
free text keywords: Música -- Informàtica, Computer Science - Sound
Funded by
EC| COMPMUSIC
Project
COMPMUSIC
Computational models for the discovery of the world's music
  • Funder: European Commission (EC)
  • Project Code: 267583
  • Funding stream: FP7 | SP2 | ERC
Download fromView all 6 versions
Zenodo
Other literature type . 2017
Provider: Datacite
ZENODO
Conference object . 2017
Provider: ZENODO
Zenodo
Other literature type . 2017
Provider: Datacite

[6] D. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.

[7] A. Klapuri. Sound onset detection by applying psychoacoustic knowledge. In ICASSP, Phoenix, USA, 1999.

[8] Anna M. Kruspe. Keyword spotting in a-capella singing. In ISMIR, Taipei, Taiwan, 2014. [OpenAIRE]

[9] Emilio Molina, Ana M. Barbancho, Lorenzo J. Tardn, and Isabel Barbancho. Evaluation Framework for Automatic Singing Transcription. In ISMIR, Taipei, Taiwan, 2014. [OpenAIRE]

[10] N. Obin, F. Lamare, and A. Roebel. Syll-O-Matic: An adaptive time-frequency representation for the automatic segmentation of speech into syllables. In ICASSP, Vancouver, Canada, 2013. [OpenAIRE]

[11] Jordi Pons and Xavier Serra. Designing efficient architectures for modeling temporal features with convolutional neural networks. In ICASSP, New orleans, USA, 2017.

[12] Jordi Pons, Olga Slizovskaia, Rong Gong, Emilia Go´mez, and Xavier Serra. Timbre analysis of music audio signals with convolutional neural networks. arxiv:1703.06697, 2017. [OpenAIRE]

[13] L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-286, February 1989.

[14] Rafael Caro Repetto and Xavier Serra. Creating a Corpus of Jingju (Beijing Opera) Music and Possibilities for Melodic Analysis. In ISMIR, Taipei, Taiwan, 2014. [OpenAIRE]

[15] Odette Scharenborg, Vincent Wan, and Mirjam Ernestus. Unsupervised speech segmentation: An analysis of the hypothesized phone boundaries. The Journal of the Acoustical Society of America, 127(2):1084-1095, 2010. [OpenAIRE]

[16] J. Schlu¨ter and S. Bo¨ck. Improved musical onset detection with convolutional neural networks. In ICASSP, Florence, Italy, 2014.

[17] Chee-Chuan Toh, Bingjun Zhang, and Ye Wang. Multiple-feature fusion based onset detection for solo singing voice. In ISMIR, Philadelphia, USA, 2008. [OpenAIRE]

[18] Elizabeth Wichmann. Listening to Theatre: The Aural Dimension of Beijing Opera. University of Hawaii Press, 1991.

Powered by OpenAIRE Research Graph
Any information missing or wrong?Report an Issue