Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription

Valero-Mas JJ, Benetos E, Iñesta JM. Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription. In: AES Conference: 2017 AES International Conference on Semantic Audio. Audio Engineering Society; 2017. Paper P2-4. Available from: https://aes.org/publications/elibrary-page/?id=18774

@inproceedings{ValeroMas2017_18774,
  author = {Valero-Mas, Jose J. and Benetos, Emmanouil and Iñesta, Jose M.},
  title = {{Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription}},
  booktitle = {AES Conference: 2017 AES International Conference on Semantic Audio},
  note = {Paper P2-4},
  year = {2017},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18774}
}

TY  - CPAPER
TI  - Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription
AU  - Valero-Mas, Jose J.
AU  - Benetos, Emmanouil
AU  - Iñesta, Jose M.
T2  - AES Conference: 2017 AES International Conference on Semantic Audio
M1  - Paper P2-4
PY  - 2017
DA  - 2017/06/06
UR  - https://aes.org/publications/elibrary-page/?id=18774
PB  - Audio Engineering Society
LA  - en
AB  - In Automatic Music Transcription, onset information is useful for correcting timing issues of multi-pitch estimation processes and obtain note-level representations of audio signals. Although this idea has been often used in transcription systems, it is still unclear to which degree its use is beneficial. We address this question by studying the influence of the accuracy of onset information in piano music transcription. Results indicate that note tracking results improve when the onset information provided is accurately estimated and properly included with the correct strategy. Additionally, results depict an important accuracy gap in note tracking when considering ground-truth onset information compared to using an automatic onset estimation algorithm, showing the need for more accurate onset detection methods for music transcription systems.
ER  -