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