FIR filter bank using adaptive algorithm for Audiogram ... · In this paper non-uniform filter bank using two half-band prototype filters are presented, filter complexity and matching

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.

http://www.ijettjournal.org/



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]




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




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)




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




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




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 1000 2000 3000 4000 5000 6000 7000 8000 9000-1

0

1

orignal audiogram

0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1

0

1

noisy signal

0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1

0

1

DWT filtered

0 5000 10000-1

-0.5

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-0.5

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0 1000 2000 3000 4000 5000 6000 7000 8000 9000-1

-0.8

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-0.2

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0.8

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DWT-FIR

DWT-FIR-RLS




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FIR filter bank using adaptive algorithm for Audiogram ... · In this paper non-uniform filter bank using two half-band prototype filters are presented, filter complexity and matching