Cost-effective sparsity aware acoustic feedback canceller

Yıl 2024, Cilt: 13 Sayı: 4, 1 - 1

Yusuf Eren , Engin Cemal Mengüç

https://doi.org/10.28948/ngumuh.1519986

Öz

One of the important problems encountered in acoustic feedback canceller (AFC) systems is the AF path has a sparse nature. This situation deteriorates the convergence rate and steady-state error of AFC systems. On the other hand, large-scale source signals such as speech/music and using high filter orders increase the computational cost of AFC systems. To this end, in this study, a cost-effective
-norm-online censoring (OC)-least mean square (LMS)
( -OC-LMS) based AFC system ( -OC-AFC) is proposed, which solves the sparse problem of the AF path by processing only informative data instead of all the data. Thus, the proposed AFC system significantly contributes to reducing the computational complexity without sacrificing its performance. This is achieved by combining the OC strategy and the penalty norm promoting sparsity. The proposed -OC-AFC system is comprehensively tested in terms of the misalignment (MIS) and the added stable gain (ASG) on real-world long sparse AF paths measured from a behind-the-ear hearing aid. Simulation results reveal the effectiveness of the proposed -OC-AFC system.

Anahtar Kelimeler

Adaptive filter, Sparsity, Least mean square algorithm, Acoustic feedback canceller, Online censoring strategy

Destekleyen Kurum

Scientific Research Projects Coordination Unit of Kayseri University

Proje Numarası

FBA-2022-1094

Teşekkür

This work was partially supported by the Scientific Research Projects Coordination Unit of Kayseri University (grant: FBA-2022-1094).

Uygun maliyetli seyrekliğe duyarlı akustik geri besleme giderici

Öz

Akustik geri besleme giderici (acoustic feedback canceller, AFC) sistemlerinde karşılaşılan önemli sorunlardan biri AF yolunun seyrek bir doğaya sahip olmasıdır. Bu durum AFC sistemlerinin yakınsama oranını ve kararlı durum hatasını kötüleştirir. Öte yandan konuşma/müzik gibi büyük ölçekli kaynak sinyalleri ve yüksek filtre derecelerinin kullanılması AFC sistemlerinin hesaplama maliyetini artırmaktadır. Bu amaçla, bu çalışmada, uygun maliyetli
-norm-çevrim içi sansürleme (online censoring, OC)-en küçük ortalama kare (least mean square, LMS)
( -OC-LMS) tabanlı AFC sistemi ( -OC-AFC) önerilmiştir. Tüm veriler yerine yalnızca bilgilendirici verileri işleyerek AF yolunun seyrek sorununu ortadan kaldırır. Böylece önerilen AFC sistemi, performansından ödün vermeden hesap yükünün azaltılmasına önemli ölçüde katkıda bulunur. Bu, OC stratejisi ve seyrekliği teşvik eden ceza normunun birleştirilmesiyle elde edilir. Önerilen -OC-AFC sistemi, kulak arkası işitme cihazından ölçülen gerçek dünyadaki uzun seyrek AF yolları üzerinde yanlış ayarlama (misalignment, MIS) ve eklenmiş sabit kazanç (added stable gain, ASG) açısından kapsamlı bir şekilde test edilmiştir. Benzetim sonuçları önerilen -OC-AFC sisteminin etkinliğini ortaya koymaktadır.

Anahtar Kelimeler

Adaptif filtre, Seyreklik, En küçük kare algoritması, Akustik geri besleme giderici, Çevrim içi sansürleme stratejisi

Destekleyen Kurum

Kayseri Üniversitesi Bilimsel Araştırma Projeleri Birimi

Proje Numarası

FBA-2022-1094

Teşekkür

Bu çalışma, Kayseri Üniversitesi Bilimsel Araştırma Projeleri Birimi tarafından FBA-2022-1094 kodlu proje ile kısmen desteklenmiştir.

Kaynakça

• R. Vanamadi and A. Kar, Feedback cancellation in digital hearing aids using convex combination of proportionate adaptive algorithms, Applied Acoustics, 182, 108175, 2021. https://doi.org/10.1016/j.apacoust. 2021.108175.
• M. G. Siqueira and A. Alwan, Steady-state analysis of continuous adaptation in acoustic feedback reduction systems for hearing-aids, IEEE Transactions on Speech and Audio Processing, 8 (4), 443-453, 2000. https://doi.org/10.1109/89.848225.
• S. S. Bhattacharjee, S. Pradhan and N. V. George, Design of a class of zero attraction based sparse adaptive feedback cancellers for assistive listening devices, Applied Acoustics, 173, 107683, 2021. https://doi.org/10.1016/j.apacoust.2020.107683.
• S. Pradhan, V. Patel, K. Patel, J. Maheshwari and N. V. George, Acoustic feedback cancellation in digital hearing aids: A sparse adaptive filtering approach, Applied Acoustics, 122, 138-145, 2017. https://doi.org/10.1016/j.apacoust.2017.02.018.
• T. van Waterschoot and M. Moonen, Fifty years of acoustic feedback control: State of the art and future challenges, Proceedings of the IEEE, 99 (2), 288-327, 2011. https://doi.org/10.1109/jproc.2010.2090998.
• J. M. Kates, Feedback cancellation in hearing aids, International Conference on Acoustics, Speech, and Signal Processing, Albuquerque (ICASSP), NM, USA, 1125-1128, 1990. https://doi.org/10.1109/icassp.1990.116141.
• J. Hellgren, Analysis of feedback cancellation in hearing aids with Filtered-X LMS and the direct method of closed loop identification, IEEE Transactions on Speech and Audio Processing, 10 (2), 119-131, 2002. https://doi.org/10.1109/89.985549.
• G. Rombouts, T. Van Waterschoot, K. Struyve and M. Moonen, Acoustic feedback cancellation for long acoustic paths using a nonstationary source model, IEEE Transactions on Signal Processing, 54 (9), 3426-3433, 2006. https://doi.org/10.1109/tsp.2006.879251.
• T. van Waterschoot and M. Moonen, Adaptive feedback cancellation for audio applications, Signal Processing, 89 (11), 2185-2201, 2009. https://doi.org/10.1016/j.sigpro.2009.04.036.
• L. Thi, T. Tran and S. E. Nordholm, A switched algorithm for adaptive feedback cancellation using pre-filters in hearing aids, Audiology Research, 11 (3), 389-409, 2021. https://doi.org/10.3390/audiolres11030037.
• L. T. T. Tran, S. E. Nordholm, H. Schepker, H. H. Dam and S. Doclo, Two-microphone hearing aids using prediction error method for adaptive feedback control, IEEE/ACM Transactions on Audio, Speech and Language Processing, 26 (5), 909-923, 2018. https://doi.org/10.1109/taslp.2018.2798822.
• A. Anand, A. Kar and M. N. S. Swamy, Design and analysis of a BLPC vocoder-based adaptive feedback cancellation with probe noise, Applied Acoustics, 115, 196-208, 2017, https://doi.org/10.1016/j.apacoust.2016.08.023.
• E. C. Mengüç and N. Acır, A novel adaptive filter design using Lyapunov stability theory, Turkish Journal of Electrical Engineering and Computer Sciences, 23 (3), 719-728, 2015. https://doi.org/10.3906/elk-1212-29.
• E. C. Mengüç and N. Acır, Lyapunov stability theory based adaptive filter algorithm for noisy measurements, 15th International Conference on Computer Modelling and Simulation (UKSim), Cambridge, UK 451-454, 2013. https://doi.org/10.1109/uksim.2013.50.
• Y. Chen, Y. Gu and A. O. Hero, Sparse LMS for system identification, International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan, 3125-3128, 2009. https://doi.org/10.1109/icassp.2009.4960286.
• Y. Gu, J. Jin and S. Mei, ℓ0 norm constraint LMS algorithm for sparse system identification, IEEE Signal Processing Letter, 16 (9), 774-777, 2009. https://doi.org/10.1109/lsp.2009.2024736.
• Vasundhara, N. B. Puhan and G. Panda, Zero attracting proportionate normalized subband adaptive filtering technique for feedback cancellation in hearing aids, Applied Acoustics, 149, 39-45, 2019. https://doi.org/10.1016/j.apacoust.2018.12.040.
• C. H. Lee, B. D. Rao and H. Garudadri, Sparsity promoting LMS for adaptive feedback cancellation, 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, 226-230, 2017. https://doi.org/10.23919/eusipco.2017.8081202.
• Vasundhara, G. Panda and N. B. Puhan, A VSS sparseness controlled algorithm for feedback suppression in hearing aids, International Symposium on Signal Processing and Information Technology (ISSPIT), 151-156, 2016. https://doi.org/10.1109/isspit.2015.7394318.
• D. Berberidis, V. Kekatos and G. B. Giannakis, Online censoring for large-scale regressions with application to streaming big data, IEEE Transactions on Signal Processing, 64 (15), 3854, 2016. https://doi.org/10.1109/tsp.2016.2546225.
• E. C. Mengüç, S. Çınar, M. Xiang and D. P. Mandic, Online censoring based weighted-frequency fourier linear combiner for estimation of pathological hand tremors, IEEE Signal Processing Letter, 28, 1460-1464, 2021. https://doi.org/10.1109/lsp.2021.3097279.
• A. O. Sarp, E. C. Mengüç, M. Peker and B. Çolak Güvenç, Data-adaptive censoring for short-term wind speed predictors based on MLP, RNN, and SVM, IEEE Systems Journal, 16 (3), 3625-3634, 2022. https://doi.org/10.1109/jsyst.2022.3150749.
• B. Çolak Güvenç, Y. Eren and E. C. Mengüç, Novel online censoring based learning algorithm for complex-valued big data streams, 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey, 1-4, 2022. https://doi.org/10.1109/siu55565.2022.9864761.
• Y. Eren, B. Çolak Güvenç and E. C. Mengüç, Online censoring based acoustic feedback cancellation for wearable hearing aids, 30th Signal Processing and Communications Applications Conference (SIU), Safranbolu, Turkey, 1-4, 2022. https://doi.org/10.1109/siu55565.2022.9864685.
• Y. Eren, B. Çolak Güvenç and E. C. Mengüç, An acoustic feedback canceler based on probe noise and informative data for hearing aids, Signal, Image Video and Processing, 18, 703-714, 2024. https://doi.org/10.1007/S11760-023-02786-7.
• Y. Eren, B. C. Guvenc, and E. C. Menguc, Cost-effective acoustic feedback cancellers for digital hearing aids, IEEE/ACM Transactions Audio, Speech and Language Processing, 32, 2367-2377, 2024. https://doi.org/10.1109/taslp.2024.3389644.
• Y. Eren, B. Çolak Güvenç and E. C. Mengüç, Cost-effective adaptive predictor for large-scale wind signal, 46th International Conference on Telecommunications and Signal Processing (TSP), Prague, Czech Republic, 221-224, 2023. https://doi.org/10.1109/tsp59544.2023.10197765.
• B. Çolak Güvenç and E. C. Mengüç, A novel family of online censoring based complex-valued least mean kurtosis algorithms, Signal Processing, 216, 109302, 2024. https://doi.org/10.1016/j.sigpro.2023.109302.
• E. C. Mengüç, N. Acır and D. P. Mandic, “A class of online censoring based quaternion-valued least mean square algorithms, IEEE Signal Processing Letter, 30, 244-248, 2023, https://doi.org/10.1109/lsp.2023.3255000.
• Y. Zhang, S. Xiao, D. Huang, D. Sun, L. Liu and H. Cui, l0 -norm penalised shrinkage linear and widely linear LMS algorithms for sparse system identification, IET Signal Processing, 11 (1), 86-94, 2017. https://doi.org/10.1049/iet-spr.2015.0218.
• D. K. Bustamante, T. L. Worrall and M. J. Williamson, Measurement and adaptive suppression of acoustic feedback in hearing aids, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3, 2017-2020, 1989. https://doi.org/10.1109/icassp.1989.266855.
• T. Sankowsky-Rothe, M. Blau, H. Schepker and S. Doclo, Reciprocal measurement of acoustic feedback paths in hearing aids, The Journal of the Acoustical Society of America, 138 (4), EL399-EL404, 2015. https://doi.org/10.1121/1.4933062.
• P. C. Loizou, Speech Enhancement: Theory and Practice (1st. edition), CRC Press, 2007. https://doi.org/10.1201/9781420015836.
• C. R. C. Nakagawa, S. Nordholm and W. Y. Yan, New insights into optimal acoustic feedback cancellation, IEEE Signal Processing Letter, 20 (9), 869-872, 2013. https://doi.org/10.1109/LSP.2013.2271318.