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. / Graduate