| Paper ID | AUD-18.3 |
| Paper Title |
ONE-SHOT CONDITIONAL AUDIO FILTERING OF ARBITRARY SOUNDS |
| Authors |
Beat Gfeller, Dominik Roblek, Marco Tagliasacchi, Google Research, Switzerland |
| Session | AUD-18: Audio and Speech Source Separation 5: Source Separation |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-SEP] Audio and Speech Source Separation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We consider the problem of separating a particular sound source from a single-channel mixture, based on only a short sample of the target source (from the same recording). Using SoundFilter, a wave-to-wave neural net- work architecture, we can train a model without using any sound class labels. Using a conditioning encoder model which is learned jointly with the source separation network, the trained model can be “configured” to filter arbitrary sound sources, even ones that it has not seen during training. Evaluated on the FSD50k dataset, our model obtains an SI-SDR improvement of 9.6 dB for mixtures of two sounds. When trained on Librispeech, our model achieves an SI-SDR improvement of 14.0 dB when separating one voice from a mixture of two speakers. Moreover, we show that the representation learned by the conditioning encoder clusters acoustically similar sounds together in the embedding space, even though it is trained without using any labels. |
