Abstract
A new speech enhancement algorithm is proposed in this paper with an aim of reducing the non-stationary noises added on the clean speech signals. The suppression of nonstationary noise is a serious problem. The attributes of noise differ according to the type of noise and environment in which the noise occurs. To solve the issue of nonstationary noises, a novel nonstationary noise suppression mechanism based on sub-band adaptive filtering (SAF) is proposed in this paper. The performance of sub band adaptive filtering is excellent in the case of speech signals when combined at very low SNR conditions. In addition to SAF, a noise classification mechanism is proposed in this paper to reduce the additional computational complexity in noise identification. Extensive simulations are performed according to the proposed mechanism by using different speech signals with different noises at different signal-to-noise ratios. The performance is evaluated in terms of the performance metrics, signal distortion, background intrusiveness, and overall quality. The proposed mechanism exhibits an outstanding performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sankar A, Beaufays SF, Digalakis V (1995) Training data clustering for improved speech recognition. In: Proceedings of European conference on speech communication and technology (EUROSPEECH), Madrid, Spain, pp 1–4
Boll SF (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech Sig Process 27(2):113–120
Ephraim Y, Malah D (1984) Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Trans Acoust Speech Sig Process 32(6):1109–1121
Saadoune A, Amrouche A, Selouani SA (2014) Perceptual subspace speech enhancement using variance of the reconstruction error. Digit Sig Process Rev J 24(1):187–196
Jensen JR, Benesty J, Christensen MG, Jingdong Chen A (2013) class of optimal rectangular filtering matrices for single-channel signal enhancement in the time domain. IEEE Trans Audio Speech Lang Process 21(12):2595–2606
Saadoune A, Amrouche A, Selouani SA (2013) MCRA noise estimation for KLT-VRE-based speech enhancement. Int J Speech Technol 16(3):333–339
Cohen I, Berdugo B (2001) Speech enhancement for non-stationary noise environments. Sig Process 81(11):2403–2418
Singh S, Tripathy M, Anand RS (2014) Single channel speech enhancement for mixed non-stationary noise environments. Adv Sig Process Intell Recogn Syst 264:545–555
Bharti SS, Gupta M, Agarwal S (2016) A new spectral subtraction method for speech enhancement using adaptive noise estimation. In: 2016 3rd international conference on recent advances in information technology (RAIT), pp 128–132
Lu C-T, Tsen K-F, Chen Y-Y, Wang L-L, Leita C-L (2016) Speech enhancement using spectral subtraction algorithm with over-subtraction and reservation factors adapted by harmonic properties. In: 2016 international conference on applied system innovation (ICASI), pp 1–5
Cohen I, Bergudo B (2002) Noise estimation by minima controlled recursive averaging for robust speech enhancement. IEEE Sig Process Lett 9(1):12–15
Siddapaji, Sudha KL (2014) Performance analysis of new time varying LMS (NTVLMS) adaptive filtering algorithm in noise cancellation system for speech enhancement. In: 2014 4th world congress on information and communication technologies (WICT 2014), pp 224–228
Wenchao X, Guangyan W, Lei C (2017) Speech enhancement algorithm based on improved variable-step LMS algorithm in cochlear implant. J Comput Appl 37(4):1212–1216
Hadei S, Iotfizad M (2010) A family of adaptive filter algorithms in noise cancellation for speech enhancement. Int J Comput Electr Eng 2
Nataraj VS, Athulya MS, Savithri SP (2017) Single channel speech enhancement using adaptive filtering and best correlating noise identification. In: 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE)
Ni J, Li F (2010) Variable regularization parameter sign subband adaptive filter. Electron Lett 46(24):1605–1607
Widrow B, Glover J, McCool JM, Kaunitz J, Williams CS, Hearn RH, Zeidler JR, Dong E, Goodlin R (1975) Adaptive noise cancelling: principles and applications. Proc IEEE 63:1692–1716
Haykin S (2014) Adaptive filter theory, 4th edn. Pearson Education Asia, LPE, London
Karthik GVS, Ajay Kumar M, Rahman MdZU (2011) Speech enhancement using gradient based variable step size adaptive filtering techniques. Int J Comput Sci Emerg Technol 2(1):168–177 (E-ISSN 2044-6004)
Yuan W, Xia B (2015) A speech enhancement approach based on noise classification. Appl Acoust 96:1119
Gerkmann T, Hendriks R (2012) Unbiased MMSE-based noise power estimation with low complexity and low tracking delay. IEEE Trans Audio Speech Lang Process 20(4):1383–1393
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Amjad Khan, G., Sreenivasa Murthy, K.E. (2020). Speech Enhancement Through an Extended Sub-band Adaptive Filter for Nonstationary Noise Environments. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-24318-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24317-3
Online ISBN: 978-3-030-24318-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)