Abstract
This paper aims to remove the noise presents in speech signals during communication in all hands-free devices like mobile phone, video conferencing, teleconferencing conferencing etc. The existing noise reduction algorithms like an adaptive filter, time-varying and multiband adaptive gain control etc., have serious drawbacks. To enhance the algorithm for a better outcome an independent component analysis (ICA) based noise reduction is used. ICA is a statistical computational technique that divides the multisource signal into individual subcomponents. It is an active approach to cancel all of the ambient noise or a selective part of it without knowing the knowledge of the background noise. The adaptive nature of ICA in the proposed method makes the algorithm more robust in a real-time scenario. In the proposed method, the noisy speech signal is maximized by using kurtosis and negentropy cost functions of ICA to separate out the original speech signal from the noise. The simulations show that the proposed adaptive ICA method provides higher SNR compared to existing ICA methods and other conventional methods. Thus Adaptive ICA performs efficient noise cancellation in all real-time communication devices.
Similar content being viewed by others
References
Arons, B. (2008). A review of the cocktail party effect. Cambridge, MA: MIT Media Lab.
Haykin, S. (1996). Adaptive filter theory, 3rd edn. New Jersey: Prentice Hall.
Proakis, J. G. (2012). Adaptive signal processing, 3rd edn. New Delhi: Perntice Hall of India.
Bregman, A. S. (2008). Auditory scene analysis. Montreal, QC: Department of Psychology, McGill University.
Benesty, J., & Chen, J. (2011). Optimal time-domain noise reduction filter. A theoretical study, VII, 79 p. 1. New York: Springer. ISBN:978-3-642-19600-3.
Chen, J., Benesty, J., & Huang, Y. (2008). A minimum distortion noise reduction algorithm with multiple microphones. IEEE Transactions on Audio, Speech, and Language Processing, 16(3), 481–493.
Chen, J., Benesty, J., Huang, Y., & Gaensler, T. (2011). On single-channel noise reduction in the time domain. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague (pp. 277–280).
Ephraim, Y., & Van Trees, H. L. (1995). A signal subspace approach for speech enhancement. IEEE Transactions on Speech and Audio Processing, 3(4), 251–266.
Hu, G., & Wang, D. (2002). Monaural speech segregation based on pitch tracking and amplitude modulation. IEEE Transactions on Neural Networks, 15(5), 1135–1150.
Huang, Y., Benesty, J., & Chen, J. (2008). Analysis and comparison of multichannel noise reduction methods in a common framework. IEEE Transactions on Audio, Speech, and Language Processing, 16(5), 957–968.
Hyvarinen, A., & Oja, E. (1997). A fast fixed-point algorithm for independent component analysis. Neural Computation, 9(7), 1483–1492.
Chen, J., Benesty, J., Huang, Y., & Doclo, S. (2006). New insights into the noise reduction Wiener filter. IEEE Transactions on Audio, Speech, and Language Processing, 14(4), 1218–1234.
Lezzoum, N., Gagnon, G., & Voix, J. (2016). Noise reduction of speech signals using time-varying and multi-band adaptive gain control for smart digital hearing protectors. Applied Acoustics. https://doi.org/10.1016/j.apacoust.2016.03.001.
Li, H., Wang, H., & Xia, B. (2006). Blind separation of noisy mixed speech signals based on wavelet transform and independent component analysis. In 8th International Conference on Signal Processing (ICSP 2006), (pp. 1–4). Beijing.
Mohanaprasad, K., & Arulmozhivarman, P. (2015). Wavelet based ICA using maximization of non-gaussianity for acoustic echo cancellation during double talk situation. Applied Acoustics (Elsevier), 97, 37–45.
Mohanaprasad, K., & Arulmozhivarman, P., & Wavelet-Based, I. C. A. (2015). Using maximum likelihood estimation and information-theoretic measure for acoustic echo cancellation during double talk situation. Circuits Systems and Signal Processing (Springer), 34(12), 3915–3931.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in space (PDF). Philosophical Magazine, 2(11), 559–572.
Schroeder, M. R. (1960). Apparatus for suppressing noise and distortion in communication signals. US Patent No. 3,180,936.
Schroeder, M. R. (1965). Processing of communication signals to reduce effects of noise. US Patent No. 3,403,224.
Soumya, R. G., Naveen, N., & Lal, M. J. (2013). Application of adaptive filter using adaptive line enhancer techniques. Third International Conference on Advances in Computing and Communications, Cochin (pp. 165–168).
Wang, D. L., & Brown, G. J. (1999). Separation of speech from interfering sounds based on oscillatory correlation. IEEE Transactions on Neural Network, 10, 684–697.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mohanaprasad, K., Singh, A., Sinha, K. et al. Noise reduction in speech signals using adaptive independent component analysis (ICA) for hands free communication devices. Int J Speech Technol 22, 169–177 (2019). https://doi.org/10.1007/s10772-019-09595-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10772-019-09595-9