Machine-learnt Beamforming for Large Aperture 3D Microphone Arrays, An Industrial Application

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

3D Microphone arrays

Industrial signal processing

Large aperture arrays

Noise cancellation

Non-negative matrix factorization

Source separation

Publikations- och innehållstyp

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