Jingju Singing Phrase Matching

The code in this repo aims to help reproduce the results in the work:

Rong Gong, Jordi Pons, and Xavier Serra. 2017. Audio to Score Matching by Combining Phonetic and Duration Information. In 18th International Society for Music Information Retrieval Conference. Suzhou, China

The objective of this research task to find the corresponding score for its singing query audio. By pre-segmenting both the singing audios and the music scores into the phrase units, we restrict this research to the "matching" scope. The matched scores could facilitate several lower-level MIR tasks, such as the score-informed automatic syllable or phoneme segmentation for singing voice.

The related code only situated in phoneticSimilarity folder. Other folders are used to test other matching methods.

Steps to reproduce the experiment results

1. Clone this repository
2. Download Jingju a capella singing dataset from http://doi.org/10.5281/zenodo.344932
3. Change dataset_path variable in general/filePath.py to locate the above dataset
4. Compile cython viterbi decoding code: go into CythonModule, in terminal type python setup.py build_ext --inplace
5. Python 2.7.9 and Essentia 2.1-beta 3 were used in the paper; Install dependencies in requirements.txt
6. Choose class_name in general/filePath.py to 'danAll' or 'laosheng' to experiment on either dan or laosheng role-type
7. Choose am in general/parameters.py to 'gmm' or 'cnn' to select acoustic model
8. Run python runHMM.py to produce the experiment results for HMM and post-processor duration modelling matching
9. Run python runHSMM.py to produce the experiment results for HSMM duration modelling matching
10. The details instructions are written in these two files above

Steps to train GMM, CNN acoustic models

1. Do steps 1, 2, 3 in Steps to reproduce the experiment results
2. To train GMM models, run python acousticModelTraining.py
3. To train CNN acoustic models, please follow the instructions in https://github.com/ronggong/EUSIPCO2017
4. After training CNN models, put them into cnnModels folders. Pre-trained models are already included.

Dependencies

numpy scipy matplotlib essentia vamp scikit-learn pinyin cython keras theano unicodecsv

License

Affero GNU General Public License version 3