@inproceedings{e4f48ce5ab144b2980c457c0cf641143,
title = "Deep Synthesizer Parameter Estimation",
abstract = "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.",
keywords = "deep parameter estimation, deep sound synthesis",
author = "Oren Barkan and David Tsiris",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8682964",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3887--3891",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "الولايات المتّحدة",
}