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Machine-learnt Beamforming for Large Aperture 3D Microphone Arrays, An Industrial Application
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- Pasha, Shahab (author)
- Mittuniversitetet,Institutionen för elektronikkonstruktion,STC
- Arian, Arian (author)
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- Lundgren, Jan, 1977- (author)
- Mittuniversitetet,Institutionen för elektronikkonstruktion,STC
- (creator_code:org_t)
- IEEE, 2021
- 2021
- English.
- In: IEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021. - : IEEE. - 9781665432870
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- An eight-element 3D microphone array is designed for source separation and noise cancellation applications in noisy and reverberant environments with multiple sound sources. In the first phase the non-negative matrix factorization is applied to each channel of the array to isolate the target signal from the mixture. In the second phase a machine learning approach is applied for designing a beamformer by the means of deep learning techniques to learn and reconstruct the target signal coefficients. The matrix factorization and machine-learnt beamforming are shown effective tools for speech and music analysis in this contribution they are adapted to a novel context of non-stationary industrial signals. It is also shown that the proposed 3D array is a more effective tool for capturing the acoustic scene compared with the 2D rectangular sub-array (only the four microphones in the front panel) in terms of noise suppression and signal quality. A comparison made the proposed machine-learnt beamforming method and the baseline analytical method suggests superior performance of the machine-learnt approach.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- 3D Microphone arrays
- Industrial signal processing
- Large aperture arrays
- Noise cancellation
- Non-negative matrix factorization
- Source separation
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- kon (subject category)
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