Skip to main content
Top
Published in: Cluster Computing 3/2019

24-07-2017

Computationally efficient generic adaptive filter (CEGAF)

Authors: Muqaddas Abid, Muhammad Ishtiaq, Farman Ali Khan, Salabat Khan, Rashid Ahmad, Peer Azmat Shah

Published in: Cluster Computing | Special Issue 3/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Enhancement to clean speech from noisy speech has always been a challenging issue for the researcher’s community. Various researchers have used different techniques to resolve this problem. These techniques can be classified into the unsupervised and supervised approaches. Amongst the unsupervised approaches, Spectral Subtraction and Wiener Filter are commonly exploited. However, such approaches do not yield significant enhancement in the speech quality as well as intelligibility. As compared to unsupervised, supervised approaches such as Hidden Markov Model produces enhanced speech signals with better quality. However, supervised approaches need prior knowledge about the type of noise which is considered their major drawback. Moreover, for each noise type, separate models need to be trained. In this paper, a novel hybrid approach for the enhancement of speech is presented to overcome the limitations of both supervised and unsupervised approaches. The filter weights adjustment on the basis of Delta Learning Rule makes it a supervised approach. To address the issue of construction of new model for each noise type, the filter adjusts its weights automatically through minimum mean square error. It is unsupervised as there is no need of estimation of noise power spectral density. Various experiments are performed to test the performance of proposed filter with respect to different parameters. Moreover, the performance of the proposed filter is compared with state-of-the-art approaches using objective and subjective measures. The results indicate that CEGAF outperforms the algorithms such as Wiener Filter, supervised NMF and online NMF.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Chabane, B., Daoued, B.: On the use of Kalman filter for enhancing speech corrupted by colored noise. WSEAS Trans. Signal Process. 4(12), 657–666 (2008) Chabane, B., Daoued, B.: On the use of Kalman filter for enhancing speech corrupted by colored noise. WSEAS Trans. Signal Process. 4(12), 657–666 (2008)
2.
go back to reference Combastel, C.: Kalman filtering and zonotopic state bounding for robust fault detection. Control and Fault-Tolerant Systems (SysTol), pp. 99–104. IEEE (2016) Combastel, C.: Kalman filtering and zonotopic state bounding for robust fault detection. Control and Fault-Tolerant Systems (SysTol), pp. 99–104. IEEE (2016)
3.
go back to reference Rehmam, B., Halim, Z., Abbas, G., Muhammad, T.: Artificial neural network-based speech recognition using dwt analysis applied on isolated words from oriental languages. Malays. J. Comput. Sci. 28(3), 242–262 (2015)CrossRef Rehmam, B., Halim, Z., Abbas, G., Muhammad, T.: Artificial neural network-based speech recognition using dwt analysis applied on isolated words from oriental languages. Malays. J. Comput. Sci. 28(3), 242–262 (2015)CrossRef
4.
go back to reference Banchhor, S., Dodia, J., Gowda, D.: GUI based performance analysis of speech enhancement techniques. Int. J. Sci. Res. Publ. 3(9), 1 (2013) Banchhor, S., Dodia, J., Gowda, D.: GUI based performance analysis of speech enhancement techniques. Int. J. Sci. Res. Publ. 3(9), 1 (2013)
5.
go back to reference Enge, P., Farmer, D., Westfall, B.: Adaptive noise cancellation. U.S. Patent No. 5,465,413. Washington, DC (1995) Enge, P., Farmer, D., Westfall, B.: Adaptive noise cancellation. U.S. Patent No. 5,465,413. Washington, DC (1995)
6.
go back to reference Mohammadiha, N., Leijon, A.: Nonnegative HMM for babble noise derived from speech HMM: application to speech enhancement. IEEE Trans. Audio Speech Lang. Process. 21(5), 998–1011 (2013)CrossRef Mohammadiha, N., Leijon, A.: Nonnegative HMM for babble noise derived from speech HMM: application to speech enhancement. IEEE Trans. Audio Speech Lang. Process. 21(5), 998–1011 (2013)CrossRef
7.
go back to reference Mohammadiha, N., Martin, R., Leijon, A.: Spectral domain speech enhancement using HMM state-dependent super-Gaussian priors. IEEE Signal Process. Lett. 20, 253–256 (2013)CrossRef Mohammadiha, N., Martin, R., Leijon, A.: Spectral domain speech enhancement using HMM state-dependent super-Gaussian priors. IEEE Signal Process. Lett. 20, 253–256 (2013)CrossRef
8.
go back to reference Mohammadiha, N., Smaragdis, P., Leijon, A.: Supervised and unsupervised speech enhancement using nonnegative matrix factorization. IEEE Trans. Audio Speech Lang. Process. 21, 2140–2151 (2013)CrossRef Mohammadiha, N., Smaragdis, P., Leijon, A.: Supervised and unsupervised speech enhancement using nonnegative matrix factorization. IEEE Trans. Audio Speech Lang. Process. 21, 2140–2151 (2013)CrossRef
9.
go back to reference Boll, S.: Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust Speech Signal Process. 27, 113–120 (1979)CrossRef Boll, S.: Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans. Acoust Speech Signal Process. 27, 113–120 (1979)CrossRef
10.
go back to reference Paurav Goel, A.G.: Review of spectral subtraction techniques for speech enhancement. Int. J. Electron. Commun. Technol. (IJECT) 2(4), 189–194 (2011) Paurav Goel, A.G.: Review of spectral subtraction techniques for speech enhancement. Int. J. Electron. Commun. Technol. (IJECT) 2(4), 189–194 (2011)
11.
go back to reference Loizou, P.C.: Speech Enhancement: Theory and Practice. CRC Press (2013) Loizou, P.C.: Speech Enhancement: Theory and Practice. CRC Press (2013)
12.
go back to reference Babu, G.R., Lavanya, D., Yamuna, B., Divya, H.: Speech enhancement using beamforming. Int. J. Eng. Comput. Sci. 4(4), 11143–11147 (2015) Babu, G.R., Lavanya, D., Yamuna, B., Divya, H.: Speech enhancement using beamforming. Int. J. Eng. Comput. Sci. 4(4), 11143–11147 (2015)
13.
go back to reference Lockwood, P., Boudy, J.: Experiments with a nonlinear spectral subtractor (NSS), Hidden Markov models and the projection, for robust speech recognition in cars. Speech Commun. 11, 215–228 (1992)CrossRef Lockwood, P., Boudy, J.: Experiments with a nonlinear spectral subtractor (NSS), Hidden Markov models and the projection, for robust speech recognition in cars. Speech Commun. 11, 215–228 (1992)CrossRef
14.
go back to reference Upadhyay, N., Jaiswal, R.K.: Single channel speech enhancement: using wiener filtering with recursive noise estimation. Procedia Comput. Sci. 84, 22–30 (2016)CrossRef Upadhyay, N., Jaiswal, R.K.: Single channel speech enhancement: using wiener filtering with recursive noise estimation. Procedia Comput. Sci. 84, 22–30 (2016)CrossRef
15.
16.
17.
go back to reference Gannot, S., Burshtein, D., Weinstein, E.: Iterative and sequential Kalman filter-based speech enhancement algorithms. IEEE Trans. Speech Audio Process. 6, 373–385 (1998)CrossRef Gannot, S., Burshtein, D., Weinstein, E.: Iterative and sequential Kalman filter-based speech enhancement algorithms. IEEE Trans. Speech Audio Process. 6, 373–385 (1998)CrossRef
18.
go back to reference Anoop, V., Rao, P.V.: Speech enhancement using hybrid optimization technique. Procedia Technol. 25, 5–11 (2016)CrossRef Anoop, V., Rao, P.V.: Speech enhancement using hybrid optimization technique. Procedia Technol. 25, 5–11 (2016)CrossRef
19.
go back to reference ur Rehman, A., Khan, F., Jadoon, B.K.: Analysis of adaptive filter and ICA for noise cancellation from a video frame. In: International Conference on Intelligent Systems Engineering (ICISE), pp. 250–255 (2016) ur Rehman, A., Khan, F., Jadoon, B.K.: Analysis of adaptive filter and ICA for noise cancellation from a video frame. In: International Conference on Intelligent Systems Engineering (ICISE), pp. 250–255 (2016)
20.
go back to reference Hu, Y., Loizou, P.C.: Subjective comparison and evaluation of speech enhancement algorithms. Speech Commun. 49, 588–601 (2007)CrossRef Hu, Y., Loizou, P.C.: Subjective comparison and evaluation of speech enhancement algorithms. Speech Commun. 49, 588–601 (2007)CrossRef
21.
go back to reference Hirsch, H.-G., Pearce, D.: The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In: ASR2000-Automatic Speech Recognition: Challenges for the new Millenium ISCA Tutorial and Research Workshop (ITRW), pp. 29–32 (2000) Hirsch, H.-G., Pearce, D.: The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In: ASR2000-Automatic Speech Recognition: Challenges for the new Millenium ISCA Tutorial and Research Workshop (ITRW), pp. 29–32 (2000)
22.
go back to reference Sameti, H., Sheikhzadeh, H., Deng, L., Brennan, R.L.: HMM-based strategies for enhancement of speech signals embedded in nonstationary noise. IEEE Trans. Speech Audio Process. 6, 445–455 (1998)CrossRef Sameti, H., Sheikhzadeh, H., Deng, L., Brennan, R.L.: HMM-based strategies for enhancement of speech signals embedded in nonstationary noise. IEEE Trans. Speech Audio Process. 6, 445–455 (1998)CrossRef
23.
go back to reference Mira, J., Sandoval, F.: From natural to artificial neural computation. International Workshop on Artificial Neural Networks, Malaga-Torremolinos, Spain, vol. 930 (1995) Mira, J., Sandoval, F.: From natural to artificial neural computation. International Workshop on Artificial Neural Networks, Malaga-Torremolinos, Spain, vol. 930 (1995)
25.
go back to reference Steel, R.G.D., Torrie, J.H.: Principles and procedures of statistics: with special reference to the biological sciences (1960) Steel, R.G.D., Torrie, J.H.: Principles and procedures of statistics: with special reference to the biological sciences (1960)
26.
go back to reference McClelland, J.L., Rumelhart, D.E., Group, P.R.: Parallel distributed processing. Explorations in the microstructure of cognition, vol. 2 (1986) McClelland, J.L., Rumelhart, D.E., Group, P.R.: Parallel distributed processing. Explorations in the microstructure of cognition, vol. 2 (1986)
27.
go back to reference Anderson, J.A., Rosenfeld, E.: Neurocomputing, vol. 2. MIT Press (1993) Anderson, J.A., Rosenfeld, E.: Neurocomputing, vol. 2. MIT Press (1993)
28.
go back to reference Rec, I.: P. 56-Objective Measurement of Active Speech Level. International Telecommunication Union, Geneva (1993) Rec, I.: P. 56-Objective Measurement of Active Speech Level. International Telecommunication Union, Geneva (1993)
29.
go back to reference Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5(6), 927–935 (1992)CrossRef Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5(6), 927–935 (1992)CrossRef
Metadata
Title
Computationally efficient generic adaptive filter (CEGAF)
Authors
Muqaddas Abid
Muhammad Ishtiaq
Farman Ali Khan
Salabat Khan
Rashid Ahmad
Peer Azmat Shah
Publication date
24-07-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1046-6

Other articles of this Special Issue 3/2019

Cluster Computing 3/2019 Go to the issue

Premium Partner