Computationally Efficient Fixed-Filter ANC for Speech Based on Long-Term Prediction for Headphone Applications
Yurii Iotov, Sidsel Marie Nørholm, Valiantsin Belyi, Mads Dyrholm, Mads Græsbøll Christensen
Abstract
In some situations, such as open office spaces, speech can play the role of an unwanted and disturbing source of noise, and ANC headphones or earbuds might help to solve this problem. However, ANC in modern headphones is often based on a pre-calculated fixed-filter for practical reasons, like stability and cost. Moreover, in some cases the optimal filter is non-causal, which cannot be realized with such a filter, and ANC attenuation performance will be significantly decreased. In this paper we propose to solve the causality problem in feedforward fixed-filter ANC systems by integrating a long-term linear prediction filter to predict the incoming disturbance, here speech, by the same amount of samples ahead in time, as the non-causal delay. The proposed ANC system outperforms conventional adaptive feedforward ANC systems in terms of computational complexity, showing comparable or better results on voiced speech attenuation at non-causal delays from 4 to 18 samples (0.5 to 2.25 ms) at a sampling frequency of 8 kHz.
BibTeX
@inproceedings{icassp2022_computationallye,
title = {Computationally Efficient Fixed-Filter ANC for Speech Based on Long-Term Prediction for Headphone Applications},
author = {Yurii Iotov and Sidsel Marie Nørholm and Valiantsin Belyi and Mads Dyrholm and Mads Græsbøll Christensen},
booktitle = {ICASSP 2022},
year = {2022}
}