A survey on deep reinforcement learning for audio‑based applications

Article

Latif, Siddique, Cuayahuitl, Heriberto, Pervez, Farrukh, Shamshad, Fahad, Ali, Hafiz Shehbaz and Cambria, Erik. 2023. "A survey on deep reinforcement learning for audio‑based applications." Artificial Intelligence Review: an international survey and tutorial journal. 56 (3), p. 2193–2240. https://doi.org/10.1007/s10462-022-10224-2
Article Title

A survey on deep reinforcement learning for audio‑based applications

ERA Journal ID17763
Article CategoryArticle
AuthorsLatif, Siddique (Author), Cuayahuitl, Heriberto (Author), Pervez, Farrukh (Author), Shamshad, Fahad (Author), Ali, Hafiz Shehbaz (Author) and Cambria, Erik (Author)
Journal TitleArtificial Intelligence Review: an international survey and tutorial journal
Journal Citation56 (3), p. 2193–2240
Number of Pages48
Year2023
Place of PublicationNetherlands
ISSN0269-2821
1573-7462
Digital Object Identifier (DOI)https://doi.org/10.1007/s10462-022-10224-2
Web Address (URL)https://link.springer.com/article/10.1007/s10462-022-10224-2
Abstract

Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels of understanding of the real world. Currently, deep learning (DL) is enabling DRL to effectively solve various intractable problems in various fields including computer vision, natural language processing, healthcare, robotics, to name a few. Most importantly, DRL algorithms are also being employed in audio signal processing to learn directly from speech, music and other sound signals in order to create audio-based autonomous systems that have many promising applications in the real world. In this article, we conduct a comprehensive survey on the progress of DRL in the audio domain by bringing together research studies across different but related areas in speech and music. We begin with an introduction to the general field of DL and reinforcement learning (RL), then progress to the main DRL methods and their applications in the audio domain. We conclude by presenting important challenges faced by audio-based DRL agents and by highlighting open areas for future research and investigation. The findings of this paper will guide researchers interested in DRL for the audio domain.

Keywords(Embodied) dialogue; Deep learning; Emotion recognition; Reinforcement learning; Speech recognition
ANZSRC Field of Research 2020461105. Reinforcement learning
461103. Deep learning
461104. Neural networks
Byline AffiliationsUniversity of Southern Queensland
University of Lincoln, United Kingdom
National University of Sciences and Technology, Pakistan
Information Technology University, Pakistan
Emulation AI, Australia
Nanyang Technological University, Singapore
Institution of OriginUniversity of Southern Queensland
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