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Paper Count: 30465
Fast Factored DCT-LMS Speech Enhancement for Performance Enhancement of Digital Hearing Aid
Abstract:Background noise is particularly damaging to speech intelligibility for people with hearing loss especially for sensorineural loss patients. Several investigations on speech intelligibility have demonstrated sensorineural loss patients need 5-15 dB higher SNR than the normal hearing subjects. This paper describes Discrete Cosine Transform Power Normalized Least Mean Square algorithm to improve the SNR and to reduce the convergence rate of the LMS for Sensory neural loss patients. Since it requires only real arithmetic, it establishes the faster convergence rate as compare to time domain LMS and also this transformation improves the eigenvalue distribution of the input autocorrelation matrix of the LMS filter. The DCT has good ortho-normal, separable, and energy compaction property. Although the DCT does not separate frequencies, it is a powerful signal decorrelator. It is a real valued function and thus can be effectively used in real-time operation. The advantages of DCT-LMS as compared to standard LMS algorithm are shown via SNR and eigenvalue ratio computations. . Exploiting the symmetry of the basis functions, the DCT transform matrix [AN] can be factored into a series of ±1 butterflies and rotation angles. This factorization results in one of the fastest DCT implementation. There are different ways to obtain factorizations. This work uses the fast factored DCT algorithm developed by Chen and company. The computer simulations results show superior convergence characteristics of the proposed algorithm by improving the SNR at least 10 dB for input SNR less than and equal to 0 dB, faster convergence speed and better time and frequency characteristics.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057000Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1825
 Shaul Florian and Neil J Bershad. " A Weighted Normalized Frequency Domain LMS Adaptive Algorithm". IEEE Transactions on Acoustics speech and signal processing. Vol. 36, no. 7, July 1998
 Francosie Beaufays. " Trasnform domain adaptive filters: An analytical approach". IEEE Transactions on Signal processing, vol. 43, no. 2, Feb 1995
 Mohammed A Shamma. " Improving the speed and performance of adaptive equalizers via transform based adaptive filtering ". 2591 Ashurst Rd. University Heights, Ohio, 44118, USA.
 Adaptive filter theory. By Simon Haykin.
 V.Udayashankara, A.P.Shivaprasad., " Digital Hearing Aid A Review", World congress on Medical physics & Biomedical Engineering. Brejil, Aug. 1994. pp. 21-26.
 V.Udayashankara , A.P.Shivaprasad. " The application of volt era LMS Adaptive filtering to speech enhancement for the Hearing Impairment ". 4th Euro-speech conference on speech communication and Technology, Mandrid, Spain. Sept.1995. pp. 18-21.
 Moore. B.C., Stainsby. T.H., Alcantara. J.I., Kuhnel. V., "The effect of speech intelligibility of varying compression time constants in a digital hearing aid". International Journal on Audio logy. 2004 Jul-Aug: 43(7), pp. 399-409.
 Chug. "Challenges and recent developments in Hearing Aids", Trends Amplif.2004:8(3). Pp. 83-124.
 Shanks. J.E., Wilson. R.H.Larson, Williams. D., " Speech recognition performance of patients with sensorineural hearing loss under unaided and aided conditions using linear and compression hearing aids". Ear hear. 2002 Aug: 23(4), pp. 280-90.
 Baer.T., Moore.B.C., Kulk.K., " Effects of low pass filtering on the intelligibility of speech in noise for people with and without dead regions at high frequencies". Journal on Acoustic soc Am. 2002 Sept: 112(3 pt 1)., pp. 1133-44.
 Hornsby.B.W., Ricketts.T.A., "The effects of compression ratio, signal-to-noise ratio and level on speech recognition in normal-hearing listners". Journal on Acoustics soc Am. 2001 June: 109(6). Pp. 2964- 73.
 Shields.P.W., Campbell.B.R., " Improvements in intelligibility of noisy reverberant speech using a binaural sub band adaptive noise-cancellation processing scheme". Journal on Acoustics Soc Am. 2001Dec: 110(6). pp. 3232-42.
 Wouters.J., Litiere.L., Van Wieringen.A., " Speech intelligibility in noisy environment with one and two microphone hearing aids". Audiology. 1999 Mar-Apr: 38(2). Pp 91-8.
 Rankovic.C.M., " Factors governing speech reception benefits of adaptive linear filtering for listeners with sensorineural loss". Jpurnal on Acoustics Soc Am. 1998 Feb: 103(2). Pp. 1043-57.
 Baer. T., Moore.B.C., Gatechouse. S., "Spectral contrast enhancement of speech in noise for listeners with sensorineural hearing impairment: effects on intelligibility, quality, and response times". Journal on Rehabilitation Res Dev. 1993:30(1). Pp.. 49-72.
 Dr. Harry Levitt, " Noise reduction in Hearing aids: An overview ". Journal of Rehabilitation Research and Development, Vol.38. No.1, Jan- 2001.
 Sunitha S L and Dr.V Udayashankara, " DFT-LMS Speech Enhancement Technique for Sensorinueural loss Patients". Journal of Bioinformatics India, Vol 3. Jan-March 2005.
 Simon Haykin, Adaptive Filter Theory", Pearson Education Asia, 4th Edition, 2002.
 Bernard Widrow and Samuel D.Stearns. " Adaptive Signal Processing", Pearson Education Asia, 2002.
 Sunitha S.L and Dr.V. Udayashankara. " DWT-LMS Speech Enhancement Technique for Performance Enhancement of Digital Hearing Aid", ICSCI, Jan 2005. (Best paper of the session has been taken for this paper)