Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio

Collins T, Böck S, Krebs F, Widmer G. Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio. In: AES Conference: 53rd International Conference: Semantic Audio. Audio Engineering Society; 2014. Paper 1-2. Available from: https://aes.org/publications/elibrary-page/?id=17096

@inproceedings{Collins2014_17096,
  author = {Collins, Tom and Böck, Sebastian and Krebs, Florian and Widmer, Gerhard},
  title = {{Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio}},
  booktitle = {AES Conference: 53rd International Conference: Semantic Audio},
  note = {Paper 1-2},
  year = {2014},
  month = jan,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17096}
}

TY  - CPAPER
TI  - Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio
AU  - Collins, Tom
AU  - Böck, Sebastian
AU  - Krebs, Florian
AU  - Widmer, Gerhard
T2  - AES Conference: 53rd International Conference: Semantic Audio
M1  - Paper 1-2
PY  - 2014
DA  - 2014/01/06
UR  - https://aes.org/publications/elibrary-page/?id=17096
PB  - Audio Engineering Society
LA  - en
AB  - Algorithms for the discovery of musical repetition have been developed in audio and symbolic domains more or less independently for over a decade. In this paper we combine algorithms for multiple F0 estimation, beat tracking, quantisation, and pattern discovery, so that for the first time, the note content of motifs, themes, and repeated sections can be discovered directly from polyphonic music audio. Testing on deadpan and expressive piano renditions of pieces, we compared pattern discovery performance against runs on symbolic representations of the same pieces. Comparing deadpan audio with deadpan-symbolic representations, establishment precision and recall fell by ~25%, and by ~50% when comparing expressive audio with deadpan-symbolic representations. The music data and evaluation results establish a benchmark for future work that attempts to bridge the audio-symbolic gap.
ER  -