DETECTION AND LOCALIZATION OF SOUND USING ADAPTIVE ... and... · Speech enhancement is one of the...

INTERNATIONAL JOURNAL OF TECHNOLOGY AND COMPUTING (IJTC)

ISSN-2455-099X,

Volume 2, Issue 6 June 2016.

IJTC201606002 www. ijtc.org 252

DETECTION AND LOCALIZATION OF

SOUND USING ADAPTIVE LEARNING

TECHNIQUE

Anil Kumar1, Dr. Manish Mann2

1Student, M.Tech, Department of CSE, L.R.I.E.T Solan, India

2Associate Professor, Department of CSE, L.R.I.E.T Solan, India

[email protected]

Abstract: In the field of artificial intelligence, Adaptive Learning Technique refers to the combination of

artificial neural networks. In this research paper the Adaptive Learning Technique has been implemented

to carry out the Detection and Localization of Sound (S). In this technique two methods are used to detect the

pure sound, In the first method wiener filter are used to reduce the amount of noise in a signal and minimize

the mean square error (M.S.E), And in the second method wiener with bacterial foraging optimization are

used for effectiveness in sound. These proposed methods are compared and the results reveal its superiority.

Keywords: Adaptive Learning Technique, Detection and Localization of Sound, Wiener filter, Wiener with

Bacterial Foraging optimization, SNR, MSE.

I. INTRODUCTION

Sound confinement is the strategy for elucidating the position of a sound source. It is an audience's capacity

which is utilized to discover or recognize the careful area or cause of an identified sound in heading and

separation. The mind uses slight contrasts in timing signs quality and unearthly to permit us to limit sound

source. The 3-dimensional position idea is utilized to express the restriction. To start with is the point of

azimuth or level edge, second is the edge of height or vertical edge and last is separation or speed. The

distinction in the passageway times between the ears, by the total sufficiency of high-recurrence sounds and

by the unpredictable unearthly reflections from different parts of our bodies, including middle, shoulders, and

so forth is known as the azimuth. Separation prompts are characterized as the loss of adequacy, loss of high

frequencies, and the straight's proportion sign to the resounded sign is known as the separation signals. These

signs are effectively interaual prompts. This is the little distinction between the two ears. It is depending on

where the source is put, in this our head goes about as an obstruction to change the tone, force, and unearthly

characteristics of the sound, assisting the with braining change where the sound radiated from. The scope of

Lower frequencies, with longer wavelengths, diffracts the sound around the head and compelling the

cerebrum to concentrate just on the staging signs from the source [1]

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a. Sound Enhancement

Speech enhancement is one of the most important topics in speech signal processing. Several techniques have

been proposed for this purpose like the spectral subtraction approach, the signal subspace approach, adaptive

noise canceling and the iterative Wiener filter. The performances of these techniques depend on the quality

and intelligibility of the processed speech signal. The improvement in the speech signal-to-noise ratio (SNR)

is the target of most techniques. Spectral subtraction is the earliest method for enhancing speech degraded by

additive noise. This technique estimates the spectrum of the clean (noise-free) signal by the subtraction of the

estimated noise magnitude spectrum from the noisy signal magnitude spectrum while keeping the phase

spectrum of the noisy signal. The drawback of this technique is the residual noise.

Another technique is the signal subspace approach. It is used for enhancing speech signals degraded by

uncorrelated additive noise or colored noise. The idea of this algorithm is based on the fact that the vector

space of the noisy signal can be decomposed into a signal plus noise subspace and an orthogonal noise

subspace. Processing is performed on the vectors in the signal plus noise subspace only, while the noise

subspace is removed first. Decomposition of the vector space of the noisy signal is performed by applying the

singular value decomposition or the Karhunen-Loeve transform (KLT) on the speech signal. Mi et al. have

proposed the signal/noise KLT based approach for the removal of colored noise [9].

b. Sound Effectiveness with Bacterial Foraging

During foraging of the real bacteria, locomotion is achieved by a set of tensile flagella. Flagella help an E.coli

bacterium to tumble or swim, which are two basic operations performed by a bacterium at the time of foraging

.When they rotate the flagella in the clockwise direction, each flagellum pulls on the cell. That results in the

moving of flagella independently and finally the bacterium tumbles with lesser number of tumbling whereas

in a harmful place it tumbles frequently to find a nutrient gradient. Moving the flagella in the

counterclockwise direction helps the bacterium to swim at a very fast rate.

In the above-mentioned algorithm the bacteria undergoes chemotaxis, where they like to move towards a

nutrient gradient and avoid noxious environment. Generally the bacteria move for a longer distance in a

friendly environment.

When they get food in sufficient, they are increased in length and in presence of suitable temperature they

break in the middle to from an exact replica of itself. This phenomenon inspired Passino to introduce an event

of reproduction in BFOA. Due to the occurrence of sudden environmental changes or attack, the chemotactic

progress may be destroyed and a group of bacteria may move to some other places or some other may be

introduced in the swarm of concern. This constitutes the event of elimination-dispersal in the real bacterial

population, where all the bacteria in a region are killed or a group is dispersed into a new part of the

environment [11]

Hamed Shamsi and I. Yucel Ozbek. Have presents most of heart sound cancellation algorithms to improve the

quality of lung sound use information about heart sound locations. Therefore, a reliable estimation of heart

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sound localizations within chest sound is a key issue to enhance the performance of heart sound cancellation

algorithms. In this paper, we present a new technique to estimate locations of heart sound segments in chest

sound using the temporal fuzzy c-means (TFCM) algorithm. [18]

A lot of research has been work out on the locations of sound, but still there are being many problems to

provide pure sound. So, to overcome these problems a proposed adaptive learning technique is used to detect

and localize the sound from different sounds and we filtering the sound interference using wiener and wiener

with bacterial foraging methods.

In this thesis adaptive learning technique has been implemented to carry out the Detection and Localization of

sound samples.

2. LITERATURE REVIEW

A lot of research has been work out on the locations of sounds but still there are being many problems to

provide pure sound.

Separation and localization of heart sound artefacts from respiratory data by a selection of eign triples in SSA,

Pulmonary auscultation is a vastly using diagnosis method over centuries. Together with the breath sound,

there is a possibility of hearing heart sound, since both sounds are originated from the human chest. For the

electronic analysis of the breath sound, separation of heart sound (HS) is important. In this paper, the

separation of HS is achieved by using a modified Singular Spectrum Analysis (SSA) method, by introducing a

provision for adaptive selection of SSA parameters [28].

Heart sound localization in chest sound using temporal fuzzy c- means classification, this paper present a new

technique to estimate locations of heart sound segments in chest sound using the temporal fuzzy c-means

(TFCM) algorithm. Therefore, reliable estimation of heart sound localizations within chest sound is a key

issue to enhance performance of heart sound cancellation algorithm [18].

Real time high accuracy 3-D PHAT based sound source localization using 4-microphone arrangement, this

paper investigates wideband sound source localization in outdoor cases. In such cases, time difference of

arrival (TDOA)-based methods are commonly used for 2-D and 3-D wideband sound source localization.

These methods have lower accuracy in comparison with direction of arrival-based approaches [16].

Detection of Heart Disease using Binary Particle Swarm Optimization, This paper introduces a

computer-aided diagnosis system of the heart valve disease using binary particle swarm optimization and

support vector machine, in conjunction with K-nearest neighbor and with leave-one-out cross-validation [17].

3. PROPOSED WORK

In our proposed method sound localization having different sound samples taken, they all are in active mode.

They produce noisy sound signals and object can identify the sound which they want to use.

3.1 Research Methodology

The object will detect and localize the sound. The object will follow adaptive learning technique; in the first

part wiener filter will reduce the amount of noise in a signal with SNR and minimize the mean square error

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(M.S.E). In the second part wiener with bacterial foraging will work, According to previous values it will

make changes until effectiveness or variations occur in the values and calculate SNR and give MSE.

The methodology of above work is divided into three parts:

I. Sound inputs

II. Algorithm Steps

III. Results and Discussions

I. Sound Inputs

Fig a. Different sound Samples

II. Algorithm Steps

Step-1. Applying adaptive learning technique to detect the sound.

Step-2. We adapted the SSA algorithm and call it adaptive learning technique.

Step-3. According just before the evaluation consequences, the arrangement concert of the future technique

was most excellent in the midst of every one categorize.

Step-4. In this research, the concept of digital signal processing is used by which we detect the sound by

applying the adaptive learning technique.

Step-5. We can reducing the interference by applying wiener filter and wiener with bacterial foraging methods

then compare that signal spectrum with database stored spectrum.

III. Results and Discussions

In this work different impure sounds samples are used as input to proposed method. This proposed method

helps to identify & localize pure sound.

a. Sound Localization

Fig b. Sound localization from D

Fig.b represents that when all the sound samples are in active mode, object D can be localized.

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Fig c. Sound localization from B

Fig.c depicted the sound samples of object B, when all the sounds are in active mode object B can be

localized.

b. Sound Detection by Wiener Method

Fig.d sound signal in time domain

Fig.d represents the noisy signal and cleaned signal graphs in terms of time domain .

Fig.e sound signal in frequency domain

Fig.e represents the signals in terms of frequency domain.

Fig. f signal to noise ratio

Fig. g represents the SNR and MSE values.

Fig. g In this SNR and MSE values are calculated.

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c. Sound Effectiveness by Wiener with Bacterial Foraging Method

Fig.h this shows how bacterias are setting its own position.

Fig.i sound signal in time domain

Fig.d represents the noisy signal and cleaned signal graphs in terms of time domain

Fig.e sound signal in frequency domain

Fig.e represents the signals in terms of frequency domain.

Fig.f Shows signal to noise ratio

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Fig.g shows variations in values.

Fig.g these figures shows the variations in values.This is the Bacterial foraging iteratively optimization

method ,This method will work according to previous values,it will make changes into previous values unitll

effectiveness occurs in the values and with this wiener will calculate SNR and MSE.

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d. Comparison between SNR and MSE for both Wiener and Wiener with Bacterial Foraging Methods.

SNR Comparison

MSE Comparison

The above tables show the comparison between SNR and MSE.

From these comparisons we can see that in the bacterial foraging optimization method, The SNR increases

means in the signal effectiveness occur and MSE is decreases. So that we can reduce interferences and the

result reveal its superiority.

CONCLUSION

The proposed method has been designed for reducing the interference by applying Adaptive learning

technique, In this technique wiener filter and wiener with bacterial foraging optimization method used. This

method is very effective for noise reducing, this method will make changes in the noisy signal until variations

occurs and give effectiveness in signal.

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http://www.researchgate.net/researcher/2039229786_Mona_Nagy_Elbedwehy

http://www.researchgate.net/researcher/63152974_Hossam_M_Zawbaa

http://www.researchgate.net/researcher/66053419_Neveen_Ghali

http://www.researchgate.net/researcher/28262044_Aboul_Ella_Hassanien

http://www.researchgate.net/publication/231181271_Detection_of_Heart_Disease_using_Binary_Particle_Swarm_Optimization



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