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Signal Processing and Machine Learning for Speech and Audio in Acoustic Sensor Networks

Besides the omnipresent mobile phones, we are surrounded by many recording devices such as laptop computers, tablets, smart watches, camcorders, and others. While many speech and audio applications have been traditionally implemented on compact devices featuring one or more microphones, wireless acoustic sensor networks (WASN) now offer a new paradigm for acoustic sensing and processing, bearing the promise to overcome the limitations of individual devices. The integration of WASNs in new speech and audio applications triggers a host of challenging research questions, solutions to which are at the core of this special issue.

This special issue invited authors to submit papers that demonstrate challenging research questions, methods and solutions for dealing with ad-hoc distributed microphone signals and applications thereof.

Lead Guest Editor
Walter Kellermann, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Guest Editors
Nobutaka Ono, Tokyo Metropolitan University, Japan
Rainer Martin, Ruhr-Universität Bochum, Germany

  2. In this paper, we propose a technique for removing a specific type of interference from a monaural recording. Nonstationary interferences are generally challenging to eliminate from such recordings. However, i...

    Authors: Takao Kawamura, Kouei Yamaoka, Yukoh Wakabayashi, Nobutaka Ono and Ryoichi Miyazaki
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:35
    Content type: Methodology Published on:

  3. In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) a...

    Authors: Eric Grinstein, Vincent W. Neo and Patrick A. Naylor
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:32
    Content type: Methodology Published on:

  4. In multichannel signal processing with distributed sensors, choosing the optimal subset of observed sensor signals to be exploited is crucial in order to maximize algorithmic performance and reduce computation...

    Authors: Michael Günther, Andreas Brendel and Walter Kellermann
    Citation: EURASIP Journal on Audio, Speech, and Music Processing 2023 2023:29
    Content type: Empirical Research Published on: