Name
Abstract | ||
|---|---|---|
A novel neural network architecture, called two-stage network (TSN), with a multi-objective learning (MOL) method for an efficient boosting strategy (BS) is proposed for speech enhancement. BS is an ensemble method using multiple base predictions (MBPs) for better final prediction. Because of the necessity for MBPs, the computational cost and model size of BS-based methods are greater than those o... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2905660 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Convolution,Feature extraction,Training,Artificial neural networks,Noise measurement,Speech enhancement,Computational efficiency | Speech enhancement,Contextual information,Pattern recognition,Convolution,Neural network architecture,Filter (signal processing),Boosting (machine learning),Artificial intelligence,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
26 | 5 | 1070-9908 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juntae Kim | 1 | 9 | 8.72 |
| Minsoo Hahn | 2 | 223 | 46.63 |