Dereverberation of Musical Instrument Recordings for Improved Note Onset Detection and Instrument Recognition

Wilmering T, Barthet M, Sandler MB. Dereverberation of Musical Instrument Recordings for Improved Note Onset Detection and Instrument Recognition. In: AES Convention 131. Audio Engineering Society; 2011. Paper 8508. Available from: https://aes.org/publications/elibrary-page/?id=16034

@inproceedings{Wilmering2011_16034,
  author = {Wilmering, Thomas and Barthet, Mathieu and Sandler, Mark B.},
  title = {{Dereverberation of Musical Instrument Recordings for Improved Note Onset Detection and Instrument Recognition}},
  booktitle = {AES Convention 131},
  note = {Paper 8508},
  year = {2011},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16034}
}

TY  - CPAPER
TI  - Dereverberation of Musical Instrument Recordings for Improved Note Onset Detection and Instrument Recognition
AU  - Wilmering, Thomas
AU  - Barthet, Mathieu
AU  - Sandler, Mark B.
T2  - AES Convention 131
M1  - Paper 8508
PY  - 2011
DA  - 2011/10/06
UR  - https://aes.org/publications/elibrary-page/?id=16034
PB  - Audio Engineering Society
LA  - en
AB  - In previous experiments it has been shown that reverberation affects the accuracy of onset detection and instrument recognition. Pre-processing a reverberated speech signal with dereverberation for automatic speech recognition (ASR), where reverberation also decreases efficiency, has been proven effective for mitigating this performance decrease. In this paper we present the results of an experimental study addressing the problem of onset detection and instrument recognition from musical signals in reverberant condition by pre-processing the audio material with a dereverberation algorithm. The experiments include four different onset detection techniques based on energy, spectrum and phase. The instrument recognition algorithm is based on line spectral frequencies (LSF) and k-means clustering. Results show improvement in onset detection performance, particularly of the spectral-based techniques. In certain conditions we also observed improvement in instrument recognition.
ER  -