Temporal Convolutions
Convolutional time-domain audio separation network
Introduced by Luo et al. in Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech SeparationCombines learned time-frequency representation with a masker architecture based on 1D dilated convolution.
Source: Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Speech Separation | 7 | 38.89% |
| Speech Enhancement | 3 | 16.67% |
| Action Detection | 1 | 5.56% |
| Activity Detection | 1 | 5.56% |
| Speaker Diarization | 1 | 5.56% |
| Denoising | 1 | 5.56% |
| Audio Source Separation | 1 | 5.56% |
| Speaker Recognition | 1 | 5.56% |
| Music Source Separation | 1 | 5.56% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
