A Deep Reinforcement Learning Approach To Audio-Based Navigation In A Multi-Speaker Environment

Giannakopoulos, Petros; Pikrakis, Aggelos; Cotronis, Yiannis

doi:10.1109/icassp39728.2021.9415013

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Research studies have also explored the potentials of DRL for solving audio localisation problems. For instance, Giannakopoulos et al (2021) trained an autonomous agent that navigates in a two-dimensional space using audio information from a multi-speaker environment. Based on their results, the authors found that the agent can successfully localise the target speaker among a set of predefined speakers in a room by avoiding confusion and going outside the predefined room boundaries.…”

Section: Other Applicationsmentioning

confidence: 99%

A survey on deep reinforcement learning for audio-based applications

Latif

Cuayáhuitl

Pervez³

et al. 2022

Artif Intell Rev

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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.

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Section: Other Applicationsmentioning

confidence: 99%

A survey on deep reinforcement learning for audio-based applications

Latif

Cuayáhuitl

Pervez³

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Their system consists of a sound propagation model and sound localization model based on classical (non-DNN) algorithms. Previous preliminary work by the authors in [5] created an autonomous agent based on deep reinforcement learning, able to navigate a virtual environment posing an audio-based navigation challenge, where a fixed number of speakers are present in a room, and guide itself towards a defined speaker while avoiding colliding with the other speakers, using only raw audio information from the environment. We extend upon this preliminary work by adding another environment focused on the task of audio source localization, unifying the training regime of the autonomous agent across both environments and studying the degree of agent knowledge transfer between the environments.…”

Section: Related Workmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments

Giannakopoulos¹,

Pikrakis²,

Cotronis³

2021

Preprint

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In this work we apply deep reinforcement learning to the problems of navigating a three-dimensional environment and inferring the locations of human speaker audio sources within, in the case where the only available information is the raw sound from the environment, as a simulated human listener placed in the environment would hear it. For this purpose we create two virtual environments using the Unity game engine, one presenting an audio-based navigation problem and one presenting an audio source localization problem. We also create an autonomous agent based on PPO online reinforcement learning algorithm and attempt to train it to solve these environments. Our experiments show that our agent achieves adequate performance and generalization ability in both environments, measured by quantitative metrics, even when a limited amount of training data are available or the environment parameters shift in ways not encountered during training. We also show that a degree of agent knowledge transfer is possible between the environments.

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Improving Post-Processing of Audio Event Detectors Using Reinforcement Learning

2022

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We apply post-processing to the class probability distribution outputs of audio event classification models and employ reinforcement learning to jointly discover the optimal parameters for various stages of a post-processing stack, such as the classification thresholds and the kernel sizes of median filtering algorithms used to smooth out model predictions. To achieve this we define a reinforcement learning environment where: 1) a state is the class probability distribution provided by the model for a given audio sample, 2) an action is the choice of a candidate optimal value for each parameter of the post-processing stack, 3) the reward is based on the classification accuracy metric we aim to optimize, which is the audio event-based macro F1-score in our case. We apply our post-processing to the class probability distribution outputs of two audio event classification models submitted to the DCASE Task4 2020 challenge. We find that by using reinforcement learning to discover the optimal per-class parameters for the post-processing stack that is applied to the outputs of audio event classification models, we can improve the audio eventbased macro F1-score (the main metric used in the DCASE challenge to compare audio event classification accuracy) by 4-5% compared to using the same post-processing stack with manually tuned parameters.INDEX TERMS audio classification, post-processing, reinforcement learning, DCASE challenge

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A Deep Reinforcement Learning Approach To Audio-Based Navigation In A Multi-Speaker Environment

Cited by 6 publications

References 8 publications

A survey on deep reinforcement learning for audio-based applications

A survey on deep reinforcement learning for audio-based applications

A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments

Improving Post-Processing of Audio Event Detectors Using Reinforcement Learning

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