Reinforcement Learning-based Microphone Selection inWireless Acoustic Sensor Networks Considering Network and Acoustic Utilities
Konferenz: Speech Communication - 14th ITG Conference
29.09.2021 - 01.10.2021 in online
Tagungsband: ITG-Fb. 298: Speech Communication
Seiten: 5Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Afifi, Haitham; Karl, Holger (Computer Networks Group, Paderborn University, Paderborn, Germany)
Guenther, Michael; Brendel, Andreas; Kellermann, Walter (Multimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-Nuernberg (FAU), Erlangen, Germany)
Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio processing applications. Due to the spatial diversity of the microphone and their relative position to the acoustic source, not all microphones are equally useful for subsequent audio signal processing tasks, nor do they all have the same wireless data transmission rates. Hence, a central task in WASNs is to balance a microphone’s estimated acoustic utility against its transmission delay, selecting a best-possible subset of microphones to record audio signals. In this work, we use reinforcement learning to decide if a microphone should be selected or switched off to maximize the acoustic quality at low transmission delays, while minimizing switching frequency. In experiments with moving sources in a simulated acoustic environment, our method outperforms naive baseline comparisons.