Restoration of Clipped Audio Signals

Year 2021, Volume: 26 Issue: 3, 1035 - 1046, 31.12.2021

Mehmet Erdal Özbek

https://doi.org/10.17482/uumfd.912949

Abstract

Restoration process is performed to remove degradations formed on the audio signals. In the restoration of clipped audio signals, which is one of these degradations, the degraded section is aimed to be restored to its original by the part of the undegraded section of the signal. The transformation of the signal from as normally given or recorded in the time domain to a different domain and thus reducing the number of samples required to be represented might be possible due to sparse representation. In this study, a restoration method is presented that relies on sparse representation of the discrete Fourier transform coefficients of the signal. In order to evaluate the performance of the proposed method, experiments were performed on various speech and music signal examples. It has been shown that the proposed method achieves better signal to noise ratio performance compared to the other methods in cases of higher clipping ratios.

Keywords

Audio restoration, Clipping, Sparse representation, Optimization

KIRPILMIŞ SES İŞARETLERİNİN YENİLENMESİ

Abstract

Ses işaretlerinde oluşan bozulmaların ortadan kaldırılması için yenileme işlemi yapılmaktadır. Bu bozulmalardan birisi olan kırpılmış ses işaretlerinin yenileme işleminde, işaretin bozulmamış bölgesindeki işaret parçası aracılığı ile işaretin bozulmaya uğramış bölgesinin özgün durumuna geri getirilmesi amaçlanmaktadır. İşaretin normal olarak verildiği ya da kayıt edildiği zaman ortamından farklı bir ortama dönüştürülmesi ve bu sayede temsil edilmesi için gerekli örnek sayısının azalması seyrek gösterim sayesinde mümkün olmaktadır. Bu çalışmada işaretin ayrık Fourier dönüşümü katsayılarının oluşturduğu seyrek gösterime dayanan bir yenileme yöntemi sunulmaktadır. Önerilen yöntemin başarımının değerlendirilmesi için farklı konuşma ve müzik işaretlerinden oluşan örnekler üzerinde çalışmalar yapılmıştır. Önerilen yöntemin işaretin daha yüksek oranda kırpılması durumunda karşılaştırılan diğer yöntemlere göre daha iyi işaret gürültü oranı başarımı elde ettiği gösterilmiştir.

Keywords

Ses yenileme, Kırpılma, Seyrek gösterim, Eniyileme

References

• 1. Abel, J. S. and Smith, J. O. (1991) Restoring a clipped signal, IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, 1745-1748. doi: 10.1109/ICASSP.1991.150655
• 2. Adler, A., Emiya, V., Jafari, M. G., Elad, M., Gribonval, R. and Plumbley, M. D. (2012) Audio inpainting, IEEE Transactions on Audio, Speech, and Language Processing, 20(3), 922-932. doi: 10.1109/TASL.2011.2168211
• 3. Candes, E. J. (2006) Compressive sampling, International Congress of Mathematicians, Madrid, 1433-1452. doi: 10.4171/022-3/69
• 4. Chen, S., Donoho, D. and Saunders, M. (1998) Atomic decomposition by basis pursuit, SIAM Journal on Scientific Computing, 20(1), 33-61. doi: 10.1137/S1064827596304010
• 5. Davis, G., Mallat, S. and Avellaneda, M. (1997) Adaptive greedy approximations, Journal of Constructive Approximation, 13, 57-98. doi: 10.1007/BF02678430
• 6. Defraene, B., Mansour, N., De Hertogh, S., van Waterschoot, T., Diehl, M. and Moonen, M. (2013) Declipping of audio signals using perceptual compressed sensing, IEEE Transactions on Audio, Speech, and Language Processing, 21(12), 2627-2637. doi: 10.1109/TASL.2013.2281570
• 7. Deruty, E. and Tardieu, D. (2014) About dynamic processing in mainstream music, Journal of Audio Engineering Society, 62(1/2), 42-55. doi: 10.17743/jaes.2014.0001
• 8. Donoho, D. (2006) Compressed sensing, IEEE Transactions on Information Theory, 52(4), 1289-1306. doi: 10.1109/TIT.2006.871582
• 9. Gaultier, C., Kitić, S., Gribonval, R. and Bertin, N. (2021) Sparsity-based audio declipping methods: Selected overview, new algorithms, and large-scale evaluation, IEEE/ACM Transactions on Audio, Speech and Language Processing, 29, 1174-1187. doi: 10.1109/TASLP.2021.3059264
• 10. Godsill, S. J. and Rayner, P. J. W. (1998) Digital Audio Restoration - A Statistical Model-Based Approach. London: Springer-Verlag. doi: 10.1007/978-1-4471-1561-8
• 11. Janssen, A. J. E. M., Veldhuis, R. N. J. and Vries, L. B. (1986) Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes, IEEE Transactions on Acoustics, Speech, and Signal Processing, 34(2), 317-330. doi: 10.1109/TASSP.1986.1164824
• 12. Kitić S., Bertin N. and Gribonval R. (2015) Sparsity and cosparsity for audio declipping: A flexible non-convex approach. in: Vincent E., Yeredor A., Koldovský Z., Tichavský P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science, 9237, 243-250. doi: 10.1007/978-3-319- 22482-4_28
• 13. Mallat, S. and Zhang, Z. (1993) Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, 41(12), 3397-3415. doi: 10.1109/78.258082
• 14. Mokrý, O. and Rajmic, P. (2020) Audio inpainting: Revisited and reweighted, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2906-2918. doi: 10.1109/TASLP.2020.3030486
• 15. Mokrý, O., Záviška, P., Rajmic, P. and Veselý, V. (2019) Introducing SPAIN (SParse Audio INpainter), 27th European Signal Processing Conference (EUSIPCO), A Coruna, 1-5. doi: 10.23919/EUSIPCO.2019.8902560
• 16. Needell, D. (2009) Noisy signal recovery via iterative reweighted L1-minimization, Forty-Third Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 113-117. doi: 10.1109/ACSSC.2009.5470154
• 17. Orcalli, A. (2001) On the methodologies of audio restoration, Journal of New Music Research, 30(4), 307-322. doi: 10.1076/jnmr.30.4.307.7496
• 18. Ozerov, A., Bilen, Ç. and Pérez, P. (2016) Multichannel audio declipping, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 659-663. doi: 10.1109/ICASSP.2016.7471757
• 19. Plumbley, M. D., Blumensath, T., Daudet, L., Gribonval, R. and Davies, M. E. (2010) Sparse representations in audio and music: From coding to source separation, Proceedings of the IEEE, 98(6), 995-1005. doi: 10.1109/JPROC.2009.2030345
• 20. Tauböck, G., Rajbamshi, S. and Balazs, P. (2021) Dictionary learning for sparse audio inpainting, IEEE Journal of Selected Topics in Signal Processing, 15(1), 104-119. doi: 10.1109/JSTSP.2020.3046422
• 21. Weinstein, A. J. and Wakin, M. B. (2011) Recovering a clipped signal in sparseland. Erişim Adresi: https://arxiv.org/pdf/1110.5063 (Erişim Tarihi: 02.09.2021).
• 22. Záviška, P., Rajmic, P., Ozerov, A. and Rencker, L. (2021) A survey and an extensive evaluation of popular audio declipping methods, IEEE Journal of Selected Topics in Signal Processing, 15(1), 5-24. doi: 10.1109/JSTSP.2020.3042071