An Adaptive Noise Canceller with Low Signal-Distortion Based on Variable Stepsize Subfilters for Human-Robot Communication

This paper proposes an adaptive noise canceller (ANC) with low signal-distortion for human-robot communication. The proposed ANC has two sets of adaptive filters for noise and crosstalk; namely, main filters (MFs) and subfilters (SFs) connected in parallel thereto. To reduce signal-distortion in the output, the stepsizes for coefficient adaptation in the MFs are controlled according to estimated signal-to-noise ratios (SNRs) of the input signals. This SNR estimation is carried out using SF output signals. The stepsizes in the SFs are determined based on the ratio of the primary and the reference input signals to cope with a wider range of SNRs. This ratio is used as a rough estimate of the input signal SNR at the primary input. Computer simulation results using TV sound and human voice recorded in a carpeted room show that the proposed ANC reduces both residual noise and signal-distortion by as much as 20 dB compared to the conventional ANC. Evaluation in speech recognition with this ANC reveals that with a realistic TV sound level, as good recognition rate as in the noise-free condition is achieved.