Name
Abstract | ||
|---|---|---|
A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation techniques with binary time-frequency masking. In the proposed method, the number of source signals is not assumed to be known in advance and the number of sources is not limited to the number of microphones. Our approach needs only two microphones and the separated sounds are maintained as stereo signals. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11679363_84 | ICA |
Keywords | Field | DocType |
convolutive speech mixture,proposed framework,underdetermined convolutive speech mixture,binary time-frequency masking,speech signal,blind source separation technique,source signal,convolutive mixture,source separation task,underdetermined blind source separation,time frequency,blind source separation | Signal processing,Underdetermined system,Masking (art),Computer science,Speech recognition,Independent component analysis,Blind signal separation,Microphone,Source separation,Binary number | Conference |
Volume | ISSN | ISBN |
3889 | 0302-9743 | 3-540-32630-8 |
Citations | PageRank | References |
4 | 0.49 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Syskind Pedersen | 1 | 81 | 11.33 |
| DeLiang Wang | 2 | 3933 | 362.87 |
| Jan Larsen | 3 | 55 | 6.62 |
| Ulrik Kjems | 4 | 150 | 40.43 |