TY - GEN
T1 - Computationally Efficient Fixed-Filter ANC for Speech Based on Long-Term Prediction for Headphone Applications
AU - Iotov, Yurii
AU - Nørholm, Sidsel Marie
AU - Belyi, Valiantsin
AU - Dyrholm, Mads
AU - Christensen, Mads Græsbøll
N1 - Funding agency: 10.13039/100017413-Innovation Fund
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - ANC headphones
KW - Causality
KW - Fixed-filter ANC
KW - Long-term linear prediction
KW - Speech attenuation
UR - http://www.scopus.com/inward/record.url?scp=85131252691&partnerID=8YFLogxK
U2 - 10.1109/icassp43922.2022.9746931
DO - 10.1109/icassp43922.2022.9746931
M3 - Article in proceeding
SN - 978-1-6654-0541-6
T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
SP - 761
EP - 765
BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PB - IEEE
T2 - 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Y2 - 23 May 2022 through 27 May 2022
ER -