Microphone utility estimation in acoustic sensor networks using single-channel signal features
Abstract 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 computational load, ideally both at the same time. In the acoustic domain, signal cross-corre...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-08-01
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| Series: | EURASIP Journal on Audio, Speech, and Music Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13636-023-00294-7 |
| _version_ | 1827709168920821760 |
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| author | Michael Günther Andreas Brendel Walter Kellermann |
| author_facet | Michael Günther Andreas Brendel Walter Kellermann |
| author_sort | Michael Günther |
| collection | DOAJ |
| description | Abstract 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 computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone signals, i.e., microphone utility, for coherent array processing, but its estimation requires that the uncoded signals are synchronized and transmitted between nodes. In resource-constrained environments like acoustic sensor networks, low data transmission rates often make transmission of all observed signals to the centralized location infeasible, thus discouraging direct estimation of signal cross-correlation. Instead, we employ characteristic features of the recorded signals to estimate the usefulness of individual microphone signals using the Magnitude-Squared Coherence (MSC) between the source and respective microphone signal as ground-truth metric. In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features. The performance of both approaches is validated experimentally using both simulated and recorded acoustic data, comprising a variety of realistic and practically relevant acoustic scenarios including moving and static sources. |
| first_indexed | 2024-03-10T17:17:10Z |
| format | Article |
| id | doaj.art-5a435b03753943208de990ba80ce0733 |
| institution | Directory Open Access Journal |
| issn | 1687-4722 |
| language | English |
| last_indexed | 2024-03-10T17:17:10Z |
| publishDate | 2023-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Audio, Speech, and Music Processing |
| spelling | doaj.art-5a435b03753943208de990ba80ce07332023-11-20T10:27:26ZengSpringerOpenEURASIP Journal on Audio, Speech, and Music Processing1687-47222023-08-012023111910.1186/s13636-023-00294-7Microphone utility estimation in acoustic sensor networks using single-channel signal featuresMichael Günther0Andreas Brendel1Walter Kellermann2Chair of Multimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-NürnbergChair of Multimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-NürnbergChair of Multimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-NürnbergAbstract 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 computational load, ideally both at the same time. In the acoustic domain, signal cross-correlation is a natural choice to quantify the usefulness of microphone signals, i.e., microphone utility, for coherent array processing, but its estimation requires that the uncoded signals are synchronized and transmitted between nodes. In resource-constrained environments like acoustic sensor networks, low data transmission rates often make transmission of all observed signals to the centralized location infeasible, thus discouraging direct estimation of signal cross-correlation. Instead, we employ characteristic features of the recorded signals to estimate the usefulness of individual microphone signals using the Magnitude-Squared Coherence (MSC) between the source and respective microphone signal as ground-truth metric. In this contribution, we provide a comprehensive analysis of model-based microphone utility estimation approaches that use signal features and, as an alternative, also propose machine learning-based estimation methods that identify optimal sensor signal utility features. The performance of both approaches is validated experimentally using both simulated and recorded acoustic data, comprising a variety of realistic and practically relevant acoustic scenarios including moving and static sources.https://doi.org/10.1186/s13636-023-00294-7Channel selectionGraph partitioningMicrophone utilityAcoustic sensor network |
| spellingShingle | Michael Günther Andreas Brendel Walter Kellermann Microphone utility estimation in acoustic sensor networks using single-channel signal features EURASIP Journal on Audio, Speech, and Music Processing Channel selection Graph partitioning Microphone utility Acoustic sensor network |
| title | Microphone utility estimation in acoustic sensor networks using single-channel signal features |
| title_full | Microphone utility estimation in acoustic sensor networks using single-channel signal features |
| title_fullStr | Microphone utility estimation in acoustic sensor networks using single-channel signal features |
| title_full_unstemmed | Microphone utility estimation in acoustic sensor networks using single-channel signal features |
| title_short | Microphone utility estimation in acoustic sensor networks using single-channel signal features |
| title_sort | microphone utility estimation in acoustic sensor networks using single channel signal features |
| topic | Channel selection Graph partitioning Microphone utility Acoustic sensor network |
| url | https://doi.org/10.1186/s13636-023-00294-7 |
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