Vibraphone transcription from noisy audio using factorization methods
| dc.contributor.author | Zehtabi, Sonmaz | |
| dc.contributor.supervisor | Tzanetakis, George | |
| dc.date.accessioned | 2012-04-30T22:34:35Z | |
| dc.date.available | 2012-04-30T22:34:35Z | |
| dc.date.copyright | 2012 | en_US |
| dc.date.issued | 2012-04-30 | |
| dc.degree.department | Department of Computer Science | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.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. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/3960 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights.temp | Available to the World Wide Web | en_US |
| dc.subject | Vibraphone | en_US |
| dc.subject | Music transcription | en_US |
| dc.subject | Noisy audio | en_US |
| dc.subject | Factorization | en_US |
| dc.title | Vibraphone transcription from noisy audio using factorization methods | en_US |
| dc.type | Thesis | en_US |