Guided Generative Adversarial Neural Network for Representation Learning and Audio Generation using Fewer Labelled Audio Data

Article


Haque, Kazi Nazmul, Rana, Rajib, Liu, Jiajun, Hansen, John H. L., Cummins, Nicholas, Busso, Carlos and Schuller, Bjorn W.. 2021. "Guided Generative Adversarial Neural Network for Representation Learning and Audio Generation using Fewer Labelled Audio Data." IEEE ACM Transactions on Audio, Speech, and Language Processing. 29, pp. 2575-2590. https://doi.org/10.1109/TASLP.2021.3098764
Article Title

Guided Generative Adversarial Neural Network for Representation Learning and Audio Generation using Fewer Labelled Audio Data

ERA Journal ID34661
Article CategoryArticle
AuthorsHaque, Kazi Nazmul (Author), Rana, Rajib (Author), Liu, Jiajun (Author), Hansen, John H. L. (Author), Cummins, Nicholas (Author), Busso, Carlos (Author) and Schuller, Bjorn W. (Author)
Journal TitleIEEE ACM Transactions on Audio, Speech, and Language Processing
Journal Citation29, pp. 2575-2590
Number of Pages16
Year2021
Place of PublicationPiscataway, United States
ISSN1558-7916
1558-7924
2329-9290
2329-9304
Digital Object Identifier (DOI)https://doi.org/10.1109/TASLP.2021.3098764
Web Address (URL)https://ieeexplore.ieee.org/document/9492807
Abstract

The Generation power of Generative Adversarial Neural Networks (GANs) has shown great promise to learn representations from unlabelled data while guided by a small amount of labelled data. We aim to utilise the generation power of GANs to learn Audio Representations. Most existing studies are, however, focused on images. Some studies use GANs for speech generation, but they are conditioned on text or acoustic features, limiting their use for other audio, such as instruments, and even for speech where transcripts are limited. This paper proposes a novel GAN-based model that we named Guided Generative Adversarial Neural Network (GGAN), which can learn powerful representations and generate good-quality samples using a small amount of labelled data as guidance. Experimental results based on a speech [Speech Command Dataset (S09)] and a non-speech [Musical Instrument Sound dataset (Nsyth)] dataset demonstrate that using only 5\% of labelled data as guidance, GGAN learns significantly better representations than the state-of-the-art models.

KeywordsGenerators, Generative adversarial networks, Spectrogram, Data models, Training, Task analysis, Speech processing
ANZSRC Field of Research 2020460212. Speech recognition
460302. Audio processing
Byline AffiliationsUniversity of Southern Queensland
Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
University of Texas, United States
King's College London, United Kingdom
Imperial College London, United Kingdom
Institution of OriginUniversity of Southern Queensland
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