Name
Abstract | ||
|---|---|---|
Monaural speech separation is a fundamental problem in speech and signal processing. This problem can be approached from a supervised learning perspective by predicting an ideal time-frequency mask from features of noisy speech. In reverberant conditions at low signal-to-noise ratios (SNRs), accurate mask prediction is challenging and can benefit from effective features. In this paper, we investig... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TASLP.2017.2687829 | IEEE/ACM Transactions on Audio, Speech, and Language Processing |
Keywords | Field | DocType |
Speech,Noise measurement,Feature extraction,Speech processing,Reverberation,Training,Spectrogram | Speech processing,Reverberation,Pattern recognition,Computer science,Spectrogram,Feature extraction,Supervised learning,Speech recognition,Artificial intelligence,Monaural,Linear predictive coding,Intelligibility (communication) | Journal |
Volume | Issue | ISSN |
25 | 5 | 2329-9290 |
Citations | PageRank | References |
8 | 0.55 | 26 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masood Delfarah | 1 | 8 | 1.23 |
| DeLiang Wang | 2 | 3933 | 362.87 |