Name
Abstract | ||
|---|---|---|
In this paper, we describe a method for recognizing sound sources in a mixture. While many audio-based content analysis methods focus on detecting or classifying target sounds in a discriminative manner, we approach this as a regression problem, in which we estimate the relative proportions of sound sources in the given mixture. Using source separation ideas based on probabilistic latent component analysis, we directly estimate these proportions from the mixture without actually separating the sources. We also introduce a method for learning a transition matrix to temporally constrain the problem. We demonstrate the proposed method on a mixture of five classes of sounds and show that it is quite effective in correctly estimating the relative proportions of the sounds in the mixture. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-28551-6_50 | LVA/ICA |
Keywords | Field | DocType |
relative proportion,source separation idea,audio-based content analysis method,classifying target,sound source,regression problem,discriminative manner,sound recognition,transition matrix,probabilistic latent component analysis | Sound recognition,Probabilistic latent component analysis,Stochastic matrix,Pattern recognition,Speech recognition,Artificial intelligence,Regression problems,Discriminative model,Source separation,Mathematics | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juhan Nam | 1 | 261 | 25.12 |
| Gautham J. Mysore | 2 | 481 | 34.53 |
| Paris Smaragdis | 3 | 1760 | 134.67 |