Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task. This paper proposes an automatic method for synthesizer parameters tuning to match a given input sound. The method is based on strided Convolutional Neural Networks and is capable of inferring the synthesizer parameters configuration from the input spectrogram and even from the raw audio. The effectiveness of our method is demonstrated on a subtractive synthesizer with frequency modulation. We present experimental results that showcase the superiority of our model over several baselines. We further show that the network depth is an important factor that contributes to the prediction accuracy.
|Title of host publication
|2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - May 2019
|44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 2019 → 17 May 2019
|ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
|44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
|12/05/19 → 17/05/19
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- deep parameter estimation
- deep sound synthesis