Name
Abstract | ||
|---|---|---|
End-to-end learning with convolutional neural networks (CNNs) has become a standard approach in image classification. However, in audio classification, CNN-based models that use time-frequency representations as input are still popular. A recently proposed CNN architecture called SampleCNN takes raw waveforms directly and has very small sizes of filters. The architecture has proven to be effective... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JSTSP.2019.2909479 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Convolution,Task analysis,Computer architecture,Neural networks,Music,Time-frequency analysis | Computer vision,Residual,Loudness,Audio signal,Pattern recognition,Spectrogram,Computer science,Convolutional neural network,Waveform,Artificial intelligence,Granularity,Contextual image classification | Journal |
Volume | Issue | ISSN |
13 | 2 | 1932-4553 |
Citations | PageRank | References |
2 | 0.40 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taejun Kim | 1 | 14 | 2.70 |
| Jongpil Lee | 2 | 111 | 15.79 |
| Juhan Nam | 3 | 261 | 25.12 |