2021
DOI: 10.48550/arxiv.2101.00240
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A Survey on Deep Reinforcement Learning for Audio-Based Applications

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Cited by 4 publications
(9 citation statements)
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“…Indeed, DRL has proved its power in developing autonomous agents in broad range of fields including audio-based applications [99], multiple agent systems [100], mobile and wireless networking [101], connected autonomous vehicles in smart cities [102], and optimal control [103].…”
Section: Deep Reinforcement Learningmentioning
confidence: 99%
“…Indeed, DRL has proved its power in developing autonomous agents in broad range of fields including audio-based applications [99], multiple agent systems [100], mobile and wireless networking [101], connected autonomous vehicles in smart cities [102], and optimal control [103].…”
Section: Deep Reinforcement Learningmentioning
confidence: 99%
“…Nevertheless, when it comes to processing raw audio waveforms with high sample rates, the limited receptive fields of CNNs can present challenges [ 6 ]. Dilated convolution layers have emerged as a solution to address this issue.…”
Section: Deep Learning Models In Audio-based Applicationsmentioning
confidence: 99%
“…Extensive research has been undertaken to explore visual, radar, radio-frequency, and audio-based methodologies, each Audio processing technology plays a ubiquitous role in our daily lives, as exemplified by the prevalence of popular products like Apple's Siri, Amazon's Alexa, and Google Home Mini Dot, which leverage audio processing and artificial intelligence (AI). AI serves as the underlying mechanism enabling computers and smartphones to comprehend human speech, thus facilitating e ective interaction between humans and machines [ 6 ]. At the core of audio-based intelligent systems lies the ability to listen to and interact with the environment, continuously learning and enhancing their responses.…”
Section: Introduction 11 Backgroundmentioning
confidence: 99%
“…Speech is the primary means of communication among human beings; as such, speech recognition systems have received considerable interest among researchers in recent decades. However, due to reliability issues, the systems developed have not been widely implemented (Latif et al, 2021;Otter et al, 2020;Strehl et al, 2006). Nevertheless, the major advancements in machine learning and deep learning in recent years have led to accurate speech recognition with high reliability that has increased the practicability of speech recognition systems (Hinton et al, 2012;Meftah et al, 2018).…”
Section: Related Studiesmentioning
confidence: 99%