Note:
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For an up-to-date list of publications from the Music Technology Group see the
Publications list
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Automatic Alignment of Long Syllables In A cappella Beijing Opera
| Title | Automatic Alignment of Long Syllables In A cappella Beijing Opera |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Conference Name | 6th International Workshop on Folk Music Analysis (FMA 2016) |
| Authors | Dzhambazov, G. , Yang Y. , Caro Repetto R. , & Serra X. |
| Pagination | 88-91 |
| Conference Start Date | 15/06/2016 |
| Conference Location | Dublin, Ireland |
| Abstract | In this study we propose how to modify a standard approach for text-to-speech alignment to apply in the case of alignment of lyrics and singing voice. We model phoneme durations by means of a duration-explicit hidden Markov model (DHMM) phonetic recognizer based on MFCCs. The phoneme durations are empirically set in a probabilistic way, based on prior knowledge about the lyrics structure and metric principles, specific for the Beijing opera music tradition. Phoneme models are GMMs trained directly on a small corpus of annotated singing voice. The alignment is evaluated on a cappella material from Beijing opera, which is characterized by its particularly long syllable durations. Results show that the incorporation of music-specific knowledge results in a very high alignment accuracy, outperforming significantly a baseline HMM-based approach. |
| preprint/postprint document | http://arrow.dit.ie/fema/1/ |
Additional material:
Dataset
The dataset consist of excerpts from 15 arias of two female singers. You can access the annotations of the dataset at http://compmusic.upf.edu/node/286 . Please refer to Section 5 in the paper for the statistics of the dataset.
Code
An efficient open-source python implementation together with documentation is available at
https://github.com/georgid/AlignmentDuration/tree/noteOnsets/src/for_jingju
