Name
Abstract | ||
|---|---|---|
Real music signals are highly variable, yet they have strong statistical structure. Prior information about the underlying physical mechanisms by which sounds are generated and rules by which complex sound structure is constructed (notes, chords, a complete musical score), can be naturally unified using Bayesian modelling techniques. Typically algorithms for Automatic Music Transcription independently carry out individual tasks such as multiple-F0 detection and beat tracking. The challenge remains to perform joint estimation of all parameters. We present a Bayesian approach for modelling music audio and content analysis. The proposed methodology based on Gaussian processes seeks joint estimation of multiple music concepts by incorporating into the kernel prior information about non-stationary behaviour, dynamics, and rich spectral content present in the modelled music signal. We illustrate the benefits of this approach via two tasks: pitch estimation and inferring missing segments in a polyphonic audio recording. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MLSP.2016.7738836 | 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | DocType | Volume |
Gaussian processes,kernel design,music signals,content analysis,audio restoration | Conference | abs/1606.01039 |
ISSN | ISBN | Citations |
2161-0363 | 978-1-5090-0747-9 | 2 |
PageRank | References | Authors |
0.38 | 14 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo A Alvarado | 1 | 3 | 1.41 |
| Dan Stowell | 2 | 209 | 21.84 |