Automatic Alignment of Long Syllables In A cappella Beijing Opera

TitleAutomatic Alignment of Long Syllables In A cappella Beijing Opera
Publication TypeConference Paper
Year of Publication2016
Conference Name6th International Workshop on Folk Music Analysis (FMA 2016)
AuthorsDzhambazov, G., Yang Y., Caro Repetto R., & Serra X.
Pagination88-91
Conference Start Date15/06/2016
Conference LocationDublin, Ireland
AbstractIn 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.
Published documenthttp://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/jingju.

Demo Video

https://vimeo.com/174680957

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