Improving Automatic Music Transcription Through Key Detection

Benetos E, Jansson A, Weyde T. Improving Automatic Music Transcription Through Key Detection. In: AES Conference: 53rd International Conference: Semantic Audio. Audio Engineering Society; 2014. Paper 3-2. Available from: https://aes.org/publications/elibrary-page/?id=17103

@inproceedings{Benetos2014_17103,
  author = {Benetos, Emmanouil and Jansson, Andreas and Weyde, Tillman},
  title = {{Improving Automatic Music Transcription Through Key Detection}},
  booktitle = {AES Conference: 53rd International Conference: Semantic Audio},
  note = {Paper 3-2},
  year = {2014},
  month = jan,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17103}
}

TY  - CPAPER
TI  - Improving Automatic Music Transcription Through Key Detection
AU  - Benetos, Emmanouil
AU  - Jansson, Andreas
AU  - Weyde, Tillman
T2  - AES Conference: 53rd International Conference: Semantic Audio
M1  - Paper 3-2
PY  - 2014
DA  - 2014/01/06
UR  - https://aes.org/publications/elibrary-page/?id=17103
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, a method for automatic transcription of polyphonic music is proposed that exploits key information. The proposed system performs key detection using a matching technique with distributions of pitch class pairs, called Zweiklang profiles. The automatic transcription system is based on probabilistic latent component analysis, supporting templates from multiple instruments, as well as tuning deviations and frequency modulations. Key information is incorporated to the transcription system using Dirichlet priors during the parameter update stage. Experiments are performed on a polyphonic, multiple-instrument dataset of Bach chorales, where it is shown that incorporating key information improves multi-pitch detection and instrument assignment performance.
ER  -