Abstract: Hearing impairment is the number one chronic
disability affecting many people in the world. 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 Hartley Transform
Power Normalized Least Mean Square algorithm (DHT-LMS) to
improve the SNR and to reduce the convergence rate of the Least
Means Square (LMS) for sensorineural loss patients. The DHT
transforms n real numbers to n real numbers, and has the convenient
property of being its own inverse. It can be effectively used for noise
cancellation with less convergence time. The simulated result shows
the superior characteristics by improving the SNR at least 9 dB for
input SNR with zero dB and faster convergence rate (eigenvalue ratio
12) compare to time domain method and DFT-LMS.
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.