Musical Polyphony Estimation

Kareer S, Basu S. Musical Polyphony Estimation. In: AES Convention 144. Audio Engineering Society; 2018. Paper 434. Available from: https://aes.org/publications/elibrary-page/?id=19547

@inproceedings{Kareer2018_19547,
  author = {Kareer, Saarish and Basu, Sattwik},
  title = {{Musical Polyphony Estimation}},
  booktitle = {AES Convention 144},
  note = {Paper 434},
  year = {2018},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19547}
}

TY  - CPAPER
TI  - Musical Polyphony Estimation
AU  - Kareer, Saarish
AU  - Basu, Sattwik
T2  - AES Convention 144
M1  - Paper 434
PY  - 2018
DA  - 2018/05/06
UR  - https://aes.org/publications/elibrary-page/?id=19547
PB  - Audio Engineering Society
LA  - en
AB  - Knowing the number of sources present in a mixture is useful for many computer audition problems such as polyphonic music transcription, source separation, and speech enhancement. Most existing algorithms for these applications require the user to provide this number thereby limiting the possibility of complete automatization. In this paper we explore a few probabilistic and machine learning approaches for an autonomous source number estimation. We then propose an implementation of a multi-class classification method using convolutional neural networks for musical polyphony estimation. In addition, we use these results to improve the performance of an instrument classifier based on the same dataset. Our final classification results for both the networks, prove that this method is a promising starting point for further advancements in unsupervised source counting and separation algorithms for music and speech.
ER  -