## SING: Symbol-to-Instrument Neural Generator

Abstract: Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as WaveNet and SampleRNN [24, 17] suffer from prohibitive training and inference times because they are based on autoregressive models that generate audio samples one at a time at a rate of 16kHz. In this work, we study the more computationally efficient alternative of generating the waveform frame-by-frame with large strides. We present SING, a lightweight neural audio synthesizer for the original task of generating musical notes given desired instrument, pitch and velocity. Our model is trained end-to-end to generate notes from nearly 1000 instruments with a single decoder, thanks to a new loss function that minimizes the distances between the log spectrograms of the generated and target waveforms. On the generalization task of synthesizing notes for pairs of pitch and instrument not seen during training, SING produces audio with significantly improved perceptual quality compared to a state-of-the-art autoencoder based on WaveNet [4] as measured by a Mean Opinion Score (MOS), and is about 32 times faster for training and 2, 500 times faster for inference.

Alexandre Defossez, Neil Zeghidour, Nicolas Usunier, Leon Bottou and Francis Bach: SING: Symbol-to-Instrument Neural Generator, Advances in Neural Information Processing Systems 31, 9041–9051, Edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi and R. Garnett, Curran Associates, Inc., 2018.
@incollection{defossez-2018,
title = {SING: Symbol-to-Instrument Neural Generator},
author = {Defossez, Alexandre and Zeghidour, Neil and Usunier, Nicolas and Bottou, Leon and Bach, Francis},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman, K. and Cesa-Bianchi, N. and Garnett, R.},
pages = {9041--9051},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://leon.bottou.org/papers/defossez-2018},
}
papers/defossez-2018.txt · Last modified: 2019/08/18 18:33 by leonb