Abstract
The structures of common multichannel processing for hearing aids include equal bandwidth (BW) finite impulse response (FIR) filter bank, nonuniform BW FIR filter bank, and fast Fourier transform (FFT) plus inverse FFT (IFFT). This paper analyzes their operation principles, indicates the design methods by means of MATLAB R2018b resources, and describes the main characteristics: synthetical ripple, bank filters’ group delays, and individual filter sidelobe attenuation. Three schemes are proposed: equal BW sixteen-filter bank, logarithmic BW eight-filter bank, and 128-point FFT plus IFFT with overlap-add operation. To build the experimental modules, we introduce the settings of spectrum scopes, the acquirement of realistic speech and noises, and the gain enhancing/reducing needs of hearing aid features; the characteristics of synthetical outputs confirm precise control ability of the multichannel modules and differences between the three schemes. Subsequently, this paper illustrates two applications of the multichannel structures in hearing aids, the equal BW sixteen-filter bank with spectral subtraction (SS) for an artificial intelligence (AI) noise reduction (NR) and 128-point FFT plus IFFT spectral distortion removal for a directional microphone (DM). In Amy’s speech mixed with ringing, milk steamer, and strong wind noises separately, the SS processor improves signal-noise-ratio (SNR) by 6.5 to 15.9 dB. By measuring waveforms and spectra at the DM input and output, the DM system seamlessly removes the spectral distortion.
Highlights
Multichannel processors have been applied to a variety of signal processing areas, such as radar return processing, speech signal processing, and hearing-aid signal processing, for several decades
Reference [1] researched broadband radar return processing, using the minimum variance distortionless response (MVDR) technique, which joints spatial-domain and frequency-domain processing so that the broadband return is split into narrow band signals by fast Fourier transform (FFT), the signals are processed in multichannel beamformers
By comparing the above schemes, we conclude that 1 in the two filter-bank schemes, the filter BW depends on the desired frequency range and number of the bank filters; but in the FFT plus inverse FFT (IFFT) scheme, the filter BW depends on number of the FFT bins and the sampling rate; usually, it is not necessary to use all the FFT bins to cover the desired frequency range
Summary
Multichannel processors have been applied to a variety of signal processing areas, such as radar return processing, speech signal processing, and hearing-aid signal processing, for several decades. Reference [6] described the features of Oticon MoreSound IntellegenceTM, whose Neural Clarity processing adopts a 24filter bank (an FFT bin is an FIR filter.) to finely analyze the incoming signals and to acquire the noise intensity and SNR in each channel; two different processing ways are to handle the speech-dominated and noise-dominated incoming signals separately; in the 24-channel SS way, a channel’s gain reduction depends on its noise intensity and SNR; the two-way outputs are prioritized to synthetically output quality speech This is referred to as Neural Clarity Process due to similarity to the human neural activities via environment training. We build two application modules, a multichannel SS for AI noise reduction (NR) and a multichannel gain balancer for directional microphone (DM) spectral distortion removal and disclose the test results
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