Vibraphone transcription from noisy audio using factorization methods
Date
2012-04-30
Authors
Zehtabi, Sonmaz
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Abstract
This thesis presents a comparison between two factorization techniques { Probabilistic Latent Component Analysis (PLCA) and Non-Negative Least Squares (NNLSQ)
{ for the problem of detecting note events played by a vibraphone, using a microphone
for sound acquisition in the context of live performance. Ambient noise is reduced by
using specifi c dictionary codewords to model the noise.
The results of the factorization are analyzed by two causal onset detection algorithms: a rule-based algorithm and a trained machine learning based classi fier. These
onset detection algorithms yield decisions on when note events happen. Comparative
results are presented, considering a database of vibraphone recordings with di fferent
levels of noise, showing the conditions under which the event detection is reliable.
Description
Keywords
Vibraphone, Music transcription, Noisy audio, Factorization