| Paper ID | MLSP-35.6 |
| Paper Title |
UNSUPERVISED MUSICAL TIMBRE TRANSFER FOR NOTIFICATION SOUNDS |
| Authors |
Jing Yang, Tristan Cinquin, ETH Zurich, Switzerland; Gábor Sörös, Nokia Bell Labs, Hungary |
| Session | MLSP-35: Independent Component Analysis |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-APPL] Applications of machine learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We present a method to transform artificial notification sounds into various musical timbres. To tackle the issues of ambiguous timbre definition, the lack of paired notification-music sample sets, and the lack of sufficient training data of notifications, we adapt the problem for a cycle-consistent generative adversarial network and train it with unpaired samples from the source and the target domains. In addition, instead of training the network with notification sound samples, we train it with video game music samples that share similar timbral features. Through a number of experiments, we discuss the efficacy of the model in transferring the timbre of monophonic and even homophonic notifications while preserving their original melody envelopes. We envision notification timbre transfer as a way of less distracting information delivery, and we demonstrate example music pieces augmented with notifications after timbre transfer. |
