Name
Abstract | ||
|---|---|---|
Given the recent surge in developments of deep learning, this paper provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross ferti... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSTSP.2019.2908700 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Deep learning,Task analysis,Convolution,Computational modeling,Hidden Markov models,Music | Computer vision,Music information retrieval,Sound detection,Computer science,Convolutional neural network,Speech recognition,Artificial intelligence,Deep learning,Artificial neural network,Audio signal processing,Memory architecture,Source separation | Journal |
Volume | Issue | ISSN |
13 | 2 | 1932-4553 |
Citations | PageRank | References |
18 | 0.80 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hendrik Purwins | 1 | 73 | 9.86 |
| Bo Li | 2 | 206 | 42.46 |
| Virtanen Tuomas | 3 | 1883 | 136.57 |
| Jan Schlüter | 4 | 231 | 18.45 |
| Shuo-Yiin Chang | 5 | 27 | 4.71 |
| Tara N. Sainath | 6 | 3497 | 232.43 |