Audio to score matching by combining phonetic and duration information
This addresses a domain-specific problem for jingju music analysis, but it is incremental as it adapts existing methods like CNNs and HMMs to a niche application.
The paper tackled the problem of matching singing phrase audio to musical scores in jingju a cappella singing by using phonetic and duration information, achieving results where CNNs performed better than DNNs and GMMs on a small dataset and HSMM outperformed a post-processor duration model.
We approach the singing phrase audio to score matching problem by using phonetic and duration information - with a focus on studying the jingju a cappella singing case. We argue that, due to the existence of a basic melodic contour for each mode in jingju music, only using melodic information (such as pitch contour) will result in an ambiguous matching. This leads us to propose a matching approach based on the use of phonetic and duration information. Phonetic information is extracted with an acoustic model shaped with our data, and duration information is considered with the Hidden Markov Models (HMMs) variants we investigate. We build a model for each lyric path in our scores and we achieve the matching by ranking the posterior probabilities of the decoded most likely state sequences. Three acoustic models are investigated: (i) convolutional neural networks (CNNs), (ii) deep neural networks (DNNs) and (iii) Gaussian mixture models (GMMs). Also, two duration models are compared: (i) hidden semi-Markov model (HSMM) and (ii) post-processor duration model. Results show that CNNs perform better in our (small) audio dataset and also that HSMM outperforms the post-processor duration model.