High-fidelity audio generation and representation learning with guided adversarial autoencoder

Article


Haque, Kazi Nazmul, Rana, Rajib and Schuller, Bjorn W.. 2020. "High-fidelity audio generation and representation learning with guided adversarial autoencoder." IEEE Access. 8, pp. 223509-223528. https://doi.org/10.1109/ACCESS.2020.3040797
Article Title

High-fidelity audio generation and representation learning with guided adversarial autoencoder

ERA Journal ID210567
Article CategoryArticle
AuthorsHaque, Kazi Nazmul (Author), Rana, Rajib (Author) and Schuller, Bjorn W. (Author)
Journal TitleIEEE Access
Journal Citation8, pp. 223509-223528
Article Number9272282
Number of Pages20
Year2020
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationPiscataway, NJ, United States
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2020.3040797
Web Address (URL)https://ieeexplore.ieee.org/document/9272282
Abstract

Generating high-fidelity conditional audio samples and learning representation from unlabelled audio data are two challenging problems in machine learning research. Recent advances in the Generative Adversarial Neural Networks (GAN) architectures show great promise in addressing these challenges. To learn powerful representation using GAN architecture, it requires superior sample generation quality, which requires an enormous amount of labelled data. In this paper, we address this issue by proposing Guided Adversarial Autoencoder (GAAE), which can generate superior conditional audio samples from unlabelled audio data using a small percentage of labelled data as guidance. Representation learned from unlabelled data without any supervision does not guarantee its' usability for any downstream task. On the other hand, during the representation learning, if the model is highly biased towards the downstream task, it losses its generalisation capability. This makes the learned representation hardly useful for any other tasks that are not related to that downstream task. The proposed GAAE model also address these issues. Using this superior conditional generation, GAAE can learn representation specific to the downstream task. Furthermore, GAAE learns another type of representation capturing the general attributes of the data, which is independent of the downstream task at hand. Experimental results involving the S09 and the NSynth dataset attest the superior performance of GAAE compared to the state-of-the-art alternatives.

Keywordsaudio generation, representation learning, generative adversarial neural network, guided generative adversarial autoencoder
ANZSRC Field of Research 2020460212. Speech recognition
461104. Neural networks
461103. Deep learning
Byline AffiliationsSchool of Sciences
Imperial College London, United Kingdom
Institution of OriginUniversity of Southern Queensland
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