Neural percussive synthesis parameterised by high-level timbral features

Ramires, António; Chandna, Pritish; Favory, Xavier; Gómez Gutiérrez, Emilia, 1975-; Serra, Xavier

doi:http://dx.doi.org/10.1109/ICASSP40776.2020.9053128

Neural percussive synthesis parameterised by high-level timbral features

Citation

Ramires A, Chandna P, Favory X, Gómez E, Serra X. Neural percussive synthesis parameterised by high-level timbral features. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2020 May 4-8; Barcelona, Spain. New Jersery: The Institute of Electrical and Electronics Engineers; 2020. p. 786-90. DOI: 10.1109/ICASSP40776.2020.9053128

Abstract

We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input parameters to the corresponding waveform. We propose two datasets to evaluate our approach on both a restrictive context, and in one covering a broader spectrum of sounds. The timbral features used as parameters are taken from recent literature in signal processing. We also use these features for evaluation and validation of the presented model, to ensure that changing the input parameters produces a congruent waveform with the desired characteristics. Finally, we evaluate the quality of the output sound using a subjective listening test. We provide sound examples and the system's source code for reproducibility.

Description

Comunicació presentada a: ICASSP 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, celebrat en línia del 4 al 8 de maig de 2020.