| Paper ID | F-3-3.1 |
| Paper Title |
NOISE SUPPRESSION USING A DIFFERENTIAL-TYPE MICROPHONE ARRAY AND TWO-DIMENSIONAL AMPLITUDE AND PHASE SPECTRA |
| Authors |
Koichiro Shiozawa, Kenji Ozawa, University of Yamanashi, Japan; Tomohiko Ise, Alps Alpine Co., Ltd., Japan |
| Session |
F-3-3: Signal Processing Systems for AI |
| Time | Thursday, 10 December, 17:30 - 19:30 |
| Presentation Time: | Thursday, 10 December, 17:30 - 17:45 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Signal Processing Systems: Design and Implementation (SPS): |
| Abstract |
This study aims to achieve noise suppression by processing the output of a microphone array with artificial neural networks (NNs). A differential-type array is used to avoid nonlinear distortions produced by a nonlinear system, such as an NN. The output of the array is considered an image, and it is transformed into a 2-dimensional (2D) spectrum. In the 2D spectrum, the frequency components of a noise are perfectly localized as direct current (DC) components along the spatial frequency axis. In this study, noise suppression was performed by spectral subtraction, after the DC components of noise were instantaneously estimated from the amplitude and phase spectra using independent NNs. As a result, the noise reduction performance of the proposed method with a 16-cm-long array was approximately 24 dB. Although the NNs were trained by white noise, the system was effective for speech and music signals as well as for white noise. |