International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 106
Abstract: Finite impulse response (FIR) filters and
filter banks contains specific properties as good
stability as well as linear-phase can be easily
achieved. hence, they are popular in many
applications such as communication systems, audio
signal processing, biomedical instruments. Based on
these properties we can implement FIR filter bank
design in Digital Hearing Aid processing. Most of the
currently available hearing aid designs provide the
filter bank with fixed bands (uniform or non-
uniform). Thus the patients unable to take the full
advantage to improve their specific auditive
performance by using the hearing aid with limited
number of fixed bands. This reduces the potential
flexibility in matching of hearing loss with steeply
sloping audiograms. One method of improving the
same is to use an instrument with higher number of
frequency bands for matching the audiogram with
minimum matching error. This proposed paper
represents an efficient FIR filter design using
adaptive algorithm to match the audiogram with
minimum errors with the filter coefficients so that the
signal to noise ratio(SNR) is increased and noise in
minimized.
Keyword: Digital Hearing Aid, Audiogram, Filter
bank, FIR, DWT, RLS algorithm
1.INRODUCTION:
Hearing aids are meant for providing hearing
assistance for the person suffering from hearing
disability. Hearing disability causes due to problem
in auditory system. The history of hearing aid is
almost a century old. Since the invention of hearing
aids the progress towards their technological growth
is immense. In the modern time Digital signal
processing(DSP) technology is widely adopted for
hearing aids. Digital signal processing approach uses
digital filters to get arbitrary frequency responses.
Linear phase is easily achieved if FIR filters are used.
An ideal hearing aid device includes several
important features as adjustable magnitude
response on different frequencies, low processing
delay, linear phase to prevent the audio signal from
distortion, noise reduction, low power consumption,
small in size programmability etc. Which can be
achieved upto The basic structure of a digital hearing
aid is shown in Fig. 2.5. The system consists of the
microphone, the analog-to-digital converter (ADC),
the digital signal processor (DSP), the digital-to-
analog converter (DAC), the receiver, and a
memory.Fig. 2. shows the structure of such a digital-
programming hearing aid device.
Figure 1 model of digital hearing device
2.AUDIOGRAM:
Loss of sensitivity to sound energy can be measured
with a simple hearing test called an audiogram Test.
An audiogram is a graph which represents one's
hearing threshold to different frequencies at different
intensities (at different pitches and different
volumes). Fig. 2. demonstrates various losses at
different frequencies and where they would be
represented in an audiogram.
FIR filter bank using adaptive algorithm for Audiogram Matching
in Digital Hearing Aid
Shobhit Kumar Nema, Mr. Amit Pathak,
Professor M.Tech, Digital communication,SRIST,Jabalpur,India,
Dept. of Electronics & communication,SRIST,Jabalpur,India.
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 107
Figure 2 audiogram chart shows the hearing loss at
different frequencies
The horizontal axis represents pitch or frequency.
The vertical axis represents loudness or intensity.
Normally 0-20dB of loss is considered as normal
hearing. 20-40 dB as Mild hearing loss. 40-55dB as
moderate hearing loss. 55-70dB as moderate-severe
loass. Loss range from 70-90dB considered as severe
hearing loss and 90-120dB lies under profound
hearing loss. Here are some sounds which lies in
audiogram graph with their respective frequency
band and intensities(threshold) given in figure.3In
Audiogram graph ('X' represents the thresholds for
the left ear and 'O' represents the thresholds for the
right ear).
Figure 3 Audiogram of familiar sounds
3. FILTER BANK REQUIREMENT:
The main task of hearing aid is to selectively amplify
the audio sounds such that the processed sound
matches one's audiogram [19{20]. To achieve this
goal, ideal hearing aid should be able to adjust sound
levels at arbitrary frequencies within a given band of
spectrum(speech frequency for audiogram test). In
practice, this is achieved by passing the input signal
through a filter bank that separates them into
different frequency bands. The gains for each
subband are adjustable as per the needs of hearing
impaired people, i.e. the amplitude response of filter
bank should equalize or `match' the audiogram.
Much effort has been invested into the design of
uniform digital filter banks for hearing aid
applications [17] [21{22]. Since hearing level
measurements are performed at each frequency as
250Hz / 500Hz / 1kHz / 2kHz / 4kHz / 8kHz in a
standard audiogram, which suggests that the uniform
filter banks will suffer difficulties in matching the
audiogram at all frequencies. Generally typical
hearing loss, especially for the cases caused by aging,
occurs at higher frequencies of speech band. To
achieve a better compensation, narrower bands need
to be allocated at high frequencies. Therefore a non-
uniform spaced digital filter bank becomes very
attractive. Both FIR filters and IIR filters are widely
used in audio applications. Therefore in this paper, a
non-uniform FIR filter bank is proposed to achieve
phase synchronization to meet the hearing
requirement. The filter bank is based on frequency-
response masking technique and provides better
matches at both low and high frequencies.
In this paper non-uniform filter bank using two half-
band prototype filters are presented, filter complexity
and matching errors is discussed. The optimization of
gains for each subband is achieved using RLS and
LMS adaptive algorithm. The DWT filter Bank is
implemented to reduce the noise and increasing SNR
(Signal to Noise Ratio). The effectiveness of the
proposed filter bank is evaluated.
4. STRUCTURE OF PROPOSED FILTER BANK
4.1 DWT Filter Bank:
The objective of our study was to evaluate wavelet
based noise reduction algorithm for use in digital
hearing aid applications. Our paper implementation is
based on close relationship between the DWT and
digital filter banks. It turns out that a tree of digital
filter banks, without computing mother wavelets, can
simply achieve the wavelet transform. Hence, the
filter banks have been playing a central role in the
area of wavelet analysis.[9]
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 108
Analysis/synthesis of DWT filter bank
The approach reduces noise by expanding the
observed speech in a series of implicitly filtered,
shift-invariant wavelet packet basis vectors.
Unwanted acoustic noise is present in a variety of
listening environments. Common examples include
the background of conversation found in a large
grpou of peaple or in restaurant; the broad spectrum
noise produced by a loud machineries in factories or
a jet engine at an airport, and the road noise heard in
a car during highspeed driving. These noises
adversely affect speech communication by
reducing the audibility of nearby speech signals.
4.2 Adaptive Filters
An adaptive filter is the filter in which the transfer
function adjusts itself according to optimization
algorithm which is driven by an error signal. The
adaptive filters make use of feedback in the form of
an error signal to modify its transfer function to be in
accordance with changing parameters. An adaptive
filter is used where the time-invariant filter cannot
satisfy the condition or fixed specifications are
unknown. An adaptive filter is a non-linear filter
because its characteristics are fully dependent on the
input signal [10]. Also the adaptive filters are time
varying as their parameters are constantly changing
in order to meet the performance criteria. Block
diagram of an adaptive filter is shown in the figure
5.1 below with input signal as x(n), y(n) is the output
of an adaptive filter, e(n) is the estimation of an error
signal, d(n) is the desired signal of finite impulse
response filter. The adaptive filter is used to
determine the difference between desired output and
an adaptive filter output. The error signal is again fed
back to an adaptive filter and its coefficients are
changed algorithmically in order to minimize this
difference.
4.3 Normalized Least Mean Square Algorithm
(NLMS) Normalized Least Mean Square algorithm is widely
used due to its computational simplicity. It can be
used for applications such as echo cancellation and
channel equalization. NLMS also has an advantage of
high convergence rate and minimum steady state
error. Despite of having these advantages the
disadvantages of Normalized Least Mean Square
algorithm cannot be ignored [13]. It requires
comparatively more number of computations for
evaluation purpose than LMS algorithm. Also in case
of NLMS the number of multiplications required is
3N+1 which is N more than LMS. The formula for
convergence factor has been modified and is given
as.
Where, μ(n)= step size
β = Normalized step size (0 < β < 2). And also the value of the weight factor can be
derived from the equations given below.
(Or)
The input x(n) is fed to an adaptive filter (n) and
simultaneously to an unknown system h(n). The
Figure 4 wavelet decomposition/reconstruction using
PRQMF bank Figure 5 adaptive filter structure
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 109
output y(n) of an unknown system is added with an
interference v(n) results with a function d(n) and then
it is subtracted from the output yˆ(n) of an adaptive
filter h(n). This difference is known as final output or
an error e(n).
4.4 Recursive Least Square Algorithm (RLS)
The recursive least square algorithm has an
advantage of fast convergence rate and is also widely
used in speech enhancement, echo cancellation and
channel equalization. It is a simple adaptive and also
time update version of pre-existing Weiner filter. In
non-stationary environment the performance of RLS
is not good as that of LMS and also due to being
sensitive to the roundoff error it leads to instability.
In RLS algorithm the filter parameters are
continuously updated with new data set without
solving the matrix inversion. Recursive Least Square
algorithm also has greater computational complexity
as compared to the Normalized Least Mean Square
algorithm as each iterations of the RLS algorithm
requires 4N 2 multiplications and 3N
2 additions
which makes the implementation of recursive least
square algorithm very costly. The complexity and
convergence delay in both NLMS and RLS
algorithms are dealt effectively by another algorithm
known as the Affine Projection Algorithm (APA).
The main idea behind the Recursive Least Square
filter is to minimize the value of cost function by
properly estimating the filter coefficients w(n) and
also updating the filter as new data or new values are
found. The error signal is e(n) and the desired signal
is d(n). the computational data for the recursive least square
algorithm is given below.
λ= Exponential weighting factor.
δ= Value used to start the inverse of
Auto correlation at n=0. i.e
P(o) = δ−1
I
The calculation of estimation error is done by the
equation given below
I= Identity matrix
P(n)=Inverse of the Auto correlation matrix Rx(n)
g(n)=gain vector.
The calculation of an estimation error is done by the equation given below.
Now the calculations of an adaptive filter
coefficient and also the coeffi-cients of an auto correlation matrix can be made by the following equations.
5. TOOL USED
For the simuatation of the proposed filter Bank
algorithm we have used MATLAB version R2015a.
The produced results are compare with the
audiograms of various patients collected from an
Audiologist (Vishal Mehra-Vaani speech & Hearing
centre, Jabalpur) . By using the algorithm coded in
MATLAB R2015a we were able to reduce the
noise efficiently and matching the audiogram with
minimum error.
6. RESULTS:
The results are so generated as we take samples of
audiogram of various patients and applied the
samples threshold over different speech frequencies.
It has been seen that the proposed filter bank
algorithm efficiently reduces the threshold value as
e(n) = d(n) − d(n)
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 110
we keep on changing the cut-off frequencies and
order of the designed filter bank.
Note: 1:All the experiment are performed for the cut-
off frequency ranging from 0.5-1 (ideally .5)
2.Results are plotted for the order of the designed
filter ranging from 5-10
3.10dB noise power which is added in audiogram
to match is AWGN.
The results of the proposed filters are given as
below:For Audiogram matching of various patients
based on the following parameters:
RK NEMA(Left ear)
Figure 6 audiogram matching for corresponding table 1
RK NEMA(Right Ear)
0
20
40
60
80
100
freq
uen
cy
25
0
50
0
10
00
20
00
40
00
80
00
actual
modified
modified
Frequency (Hz)
Actual threshold
Modified threshold
250 45 40.98
500 60 50.71
1000 60 50.97
2000 70 57.74
4000 70 57.36
8000 80 78.79
Table 2 audiogram matching of corresponding patient
Noise(db) order cutoff
10 5 0.7
Frequency (Hz)
Actual threshold
Modified threshold
250 60 52.72
500 80 66.36
1000 85 70.54
2000 75 61.66
4000 85 69.09
8000 95 90.53
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 111
Figure 7 audiogram matching for corresponding table
2
The following table of the filter parameters as
follows:
Patient Name: R K NEMA (Right ear)
The waveform of filter simulation plotted for the
above patient shown (for response of audiogram
at 1000 Hz):
FIG 8
Figure 9 3-D graph of audiogram signal
FIG 9
Figure 9 The original Audio signal
FIG 10
Figure 10 10dB noise added to original signal
0 10 20 30 40 50 60 70 80 90
freq
uen
cy
25
0
50
0
10
00
20
00
40
00
80
00
actual
modified
0 50 100 150 200 250 300 350 400 450 500
1
2
3
4
5
6
7
Frequency (Hz)
the 3d graph of audiogram signal
Tim
e
0 1000 2000 3000 4000 5000 6000 7000 8000 9000-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6the audio signal
0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1
-0.5
0
0.5
1
orignal audiogram
0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1
-0.5
0
0.5
1
noisy signal
Table 1 audiogram matching of corresponding patient
Noise(dB) order Cutoff
10 10 0.7
Frequency (Hz)
Actual threshold
Modified threshold
MSE
250 45 40.586 0.29
500 60 50.2 0.619
1000 60 50.47 0.018
2000 70 57025 0.1
4000 70 56.85 0.17
8000 80 78.29 0.94
International Journal of Engineering Trends and Technology (IJETT) – Volume-42 Number-3 - December 2016
ISSN: 2231-5381 http://www.ijettjournal.org Page 112
FIG 11
Figure 11 original noisy signal passed through DWT filter
FIG 12
Figure 12 original noisy signal passed through DWT + FIR
filter
FIG 13
Figure 13 original noisy signal processing through
DWT,FIR & Adaptive filter
FIG 14
Figure 14 final signal analysis
7.CONCLUSION:
The results obtained by using adaptive algorithm we
see that the audiogram is matched with the filter
coefficient and the noise is also reduced using DWT-
FIR design and we are able to reduce the threshold
value of respective audiogram with provides the
improvement achieving high SNR value with very
low MSE. However we can further implement
adaptive algorithm with affine algorithm to reduce
feedback noise cancellation.
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0
1
orignal audiogram
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0
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