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Using a Neural Network Codec Approximation Loss to Improve Source Separation Performance in Limited Capacity Networks
Name
IJCNN_20.pdf
Description
Accepted version
Size
2.66 MB
Format
Adobe PDF
Checksum (MD5)
ae0b4cb46b1800a5b11c575d44e30bf2
Author(s)
Ananthabhotla, I
Ewert, S
Paradiso, JA
Date Issued
2020
Journal
Proceedings of the International Joint Conference on Neural Networks
Publisher
IEEE
Citation
Ananthabhotla, I, Ewert, S and Paradiso, JA. 2020. "Using a Neural Network Codec Approximation Loss to Improve Source Separation Performance in Limited Capacity Networks." Proceedings of the International Joint Conference on Neural Networks.
Version
Author's final manuscript
Abstract
© 2020 IEEE. A growing need for on-device machine learning has led to an increased interest in light-weight neural networks that lower model complexity while retaining performance. While a variety of general-purpose techniques exist in this context, very few approaches exploit domain-specific properties to further improve upon the capacity-performance trade-off. In this paper, extending our prior work [1], we train a network to emulate the behaviour of an audio codec and use this network to construct a loss. By approximating the psychoacoustic model underlying the codec, our approach enables light-weight neural networks to focus on perceptually relevant properties without wasting their limited capacity on imperceptible signal components. We adapt our method to two audio source separation tasks, demonstrate an improvement in performance for small-scale networks via listening tests, characterize the behaviour of the loss network in detail, and quantify the relationship between performance gain and model capacity. Our work illustrates the potential for incorporating perceptual principles into objective functions for neural networks.
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/IJCNN48605.2020.9207053
