Deep Synthesizer Parameter Estimation

Oren Barkan; David Tsiris

doi:10.1109/ICASSP.2019.8682964

Deep Synthesizer Parameter Estimation

Oren Barkan, David Tsiris

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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.

Original language	English
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3887-3891
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682964
State	Published - May 2019
Externally published	Yes
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Keywords

deep parameter estimation
deep sound synthesis

Access to Document

10.1109/ICASSP.2019.8682964

Cite this

Barkan, O., & Tsiris, D. (2019). Deep Synthesizer Parameter Estimation. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 3887-3891). Article 8682964 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682964

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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.",

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author = "Oren Barkan and David Tsiris",

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Barkan, O & Tsiris, D 2019, Deep Synthesizer Parameter Estimation. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8682964, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 3887-3891, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8682964

Deep Synthesizer Parameter Estimation. / Barkan, Oren; Tsiris, David.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3887-3891 8682964 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Barkan O, Tsiris D. Deep Synthesizer Parameter Estimation. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 3887-3891. 8682964. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2019.8682964

Deep Synthesizer Parameter Estimation

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