VOICE SIGNAL SEPARATION FROM TRAIN NOISE CORRUPTED …€¦ · Ravi Kumar CV Sr. Assistant...

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International Journal of Electrical Engineering and Technology (IJEET)

Volume 11, Issue 3, May-June 2020, pp. 218-230, Article ID: IJEET_11_03_026

Available online at http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=11&IType=3

ISSN Print: 0976-6545 and ISSN Online: 0976-6553

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© IAEME Publication

VOICE SIGNAL SEPARATION FROM TRAIN

NOISE CORRUPTED SIGNAL USING NOTCH

AND ELLIPTIC FILTER

Swadha Jha

B.Tech, Electronics and Communication Engineering, VIT University, Vellore, India

Ravi Kumar CV

Sr. Assistant Professor, SENSE, VIT University, Vellore, India

ABSTRACT

Sources of noise are sound generated by the engine, heat noise, wind noise, etc. In

telecommunication the noise that effects much more to the speech signal is additive

white Gaussian noise. So, analysis of speech signal is an important factor for the

telecommunication industries. And reconstruction of the original speech signal from

the noisy speech signal passing through the channel is a very difficult task. There are

many digital and analog filters which are used for the removal of the noise signal

from speech signal. Each filters have their advantages and disadvantages. Depending

on our need we choose a suitable filter for de-noising the speech signal. In this paper,

we focus on the noise in the speech signal due to the high frequency horn of the diesel

or electric locomotives and many other external noises. First, we perform the analysis

of the spectrum by the method known as Discrete Fourier Transform (DFT) using Fast

Fourier Transform (FFT) algorithm and we performed an audio de-noising technique

based on few types of analog filters like – elliptical filter, notch filter (of different

type). We deduce an algorithm for the given filters and simulate it through the

MATLAB platform. The results of MATLAB simulation are then analyzed to give

better performance for de-noising speech signal.

Key words: Notch filter; elliptical filter; De-noising; Reconstruction; Discrete Fourier

Transform (DFT); Fast Fourier Transform (FFT).

Cite this Article: Swadha Jha and Ravi Kumar CV, Voice Signal Separation from

Train Noise Corrupted Signal Using Notch and Elliptic Filter, International Journal of

Electrical Engineering and Technology, 11(3), 2020, pp. 218-230.

http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=11&IType=3

1. INTRODUCTION

Today’s modern life is transforming into a digital life where we are totally surrounded by

digital contents like photos, videos, audios etc. and the data it contains can be anything. The

major changes that took place in any field till now is in the communication. The growing and

modern population are much more in need of the high data transfer and with great

Voice Signal Separation from Train Noise Corrupted Signal Using Notch and Elliptic Filter


performance without any loss in the data. So, to meet the requirement of the people we need a

proper system which can secure the data and transfer it without any loss and distortion of data.

The loss of data can be in many ways like addition of unwanted noise signal to the given data

or speech signal. The real life application in which we deal with the signal processing

technique are always get disturbed by noise. In this the unwanted signals which has frequency

closer to the original signal gets added to the signal, hence this leads to the signal

contamination. So, once the signal gets contaminated then it becomes important to remove the

unwanted signal from it. Basic task of signal processing is to remove this unwanted signal

from the original signal. Because of great capacity, adaptability and elite to value proportion,

DSP chip turns into the exploration focal point of the sound flag handling field. The new

results of numerous organizations advance the application look into of DSP in this field, for

instance, TI, ADI, AGERE and Motorola, and the innovations of sound coding, pressure,

distinguishing proof, upgrade, and commotion undoing have been produced advance

quickly[1].Individuals lean toward wearing earphones to tune in to music or and so on, in a

few spots, for example, auto, flying machine, and trains. Be that as it may, irritating clamors

constantly present in these situations, and individuals be influenced by commotion

obstruction. This will deliver harm on hearing. To decrease commotion, inactive clamor

dropping earphones started to be embraced. Be that as it may, they utilize physical materials

to detach some portion of the commotion with no great impact. Subsequently, dynamic

clamor decrease strategies are utilized to accomplish the protest of making the earth calm.

The human voice is a usually helpful apparatus and it is the most critical intends to pass

data to one another. Passing message by voice is the most critical and the viable route for

humankind. Presently with the improvement of the occasions, humanity has entered the data

age, with the cutting-edge methods for discourse flag think about, individuals can produce,

transmit, store, get to, and apply voice informing all the more adequately, which has an

imperative criticalness for the advancement of social improvement[2]. So in this proposition,

we fabricated a voice gathering framework, which can gather voice flag at that point break

down the flag, at that point channel the clamor by utilizing a different kind of channels.

1.1. Audio Noise

As we know that the train horn is mainly used as warning for the nearby species to keep

distance from the train. train horn has a frequency which lies in the range of 500-1500 Hz and

random train noises from the tracks and coupling also lie below 2000 kHz.

1.2. Voice Frequency

The voiced speech of a typical adult male will have a fundamental frequency from 85 to 180

Hz, and that of a typical adult female from 165 to 255 Hz. Thus, the fundamental frequency of

most speech falls below the bottom of the "voice frequency" band as defined above. However,

enough of the harmonic series will be present for the missing fundamental to create the

impression of hearing the fundamental tone. In telephony, the usable voice frequency band

ranges from approximately 300 Hz to 3400 Hz. It is for this reason that the ultra-low

frequency band of the electromagnetic spectrum between 300 and 3000 Hz is also referred to

as voice frequency, being the electromagnetic energy that represents acoustic energy at

baseband. The bandwidth allocated for a single voice-frequency transmission channel is

usually 4 kHz, including guard bands, allowing a sampling rate of 8 kHz to be used as the

basis of the pulse code modulation system used for the digital PSTN. Per the Nyquist–

Shannon sampling theorem, the sampling frequency (8 kHz) must be at least twice the highest

component of the voice frequency via appropriate filtering prior to sampling at discrete times

(4 kHz) for effective reconstruction of the voice signal.

Swadha Jha and Ravi Kumar CV


2. LITERATURE ANALYSIS

2.1. Types of Noises

The noise can be in any form either it is from machine or it is due to sudden change in the

atmospheric conditions. So, noise is characterized on different basis as: Noise generated from

electronics devices like thermal and shot noise. These noises is the cause of distortion in the

signals of Radio Frequency range. Acoustic noise exuding from moving, vibrating or

impacting sources, for example, rotating Machines, moving vehicles, console snaps, wind and

rain. These noises are the one which effect the audio signal. As it is of the range near audio

signals, it creates great impact on the audio signals. The electromagnetic noise that can

meddle with the transmission and gathering of voice, picture, and information over the radio-

recurrence range[3]. The frequency range of the electromagnetic noise is around giga hertz.

Effects the radio frequency signal and is known as Electromagnetic Interference (EMI).

Electrostatic noise is another form of noise signal generated due to presence of a potential at a

certain point. This type of noise is generally caused due to coupling of AC noise into the

system through parasitic capacitor. Quantization commotion and lost information bundles

because of system blockage. It is uniformly distributed noise signal and in communication

system it mainly effects during the analog to digital signal conversion. Signal distortion is the

term frequently used to portray a methodical unfortunate change in a signal and alludes to

changes in a signal from the non-perfect attributes of the correspondence channel, signal

blurring resonations, resound, and multipath reflections and missing examples. Also

depending on frequency, time characteristics and spectrum of the signal the noise can be

further classified into different categories: White Noise, Band-limited White Noise,

Narrowband Noise, Colored Noise, Impulsive Noise, Transient Noise pulses. Here we have

used a train signal, which is considered as a noise for our speech signal. In train signal there

are few frequency at which the power is very high. So, we focus on this frequencies and

remove them using the different notch filter. Then we remove the low frequency noise signal

using Elliptical filter. The mentioned frequency at which this noise signal is present is shown

in Table 1.

Table 1 Frequency at which noise is present.

Noise frequency

1347.95

1011.75

2021.75

673.5

474.5

337

The spectrum of train noise signal at the frequency given in table 1. is shown in Fig.1.



Figure 1 Spectrum of the noise signal at different frequency.

3. FILTERS AND IT’S DESIGN

As we know the actual meaning of filter is to separate a particular part of the group of similar

parts. In signal processing the filters are network that separates the required signal from a

group of signals of different signals. And filters are dependent on the frequency which we

want to filter through it and are independent of other frequency. The essential idea of a

channel can be clarified by analyzing the recurrence subordinate nature of the impedance of

capacitors and inductors. Consider a voltage divider where the shunt leg is responsive

impedance. As the recurrence is changed, the estimation of the responsive impedance

changes, and the voltage divider proportion changes[3,4].Filters have many applications like a

simple low pass filter of single pole is used to stabilize amplifiers by rolling off the gain at the

higher frequencies where the oscillation is caused due to excessive phase shift. High pass

filters are used to block dc offset voltage in the high gain amplifiers. Filter passes the signal of

interest and attenuates the unwanted frequencies. The low pass filter process signals with a

lower estimation of recurrence than edge lessen the flag that ought to be over the limit esteem.

The measure of these frequencies will rely upon the planning of channels. The high-pass filter

is an electronic filter process the signs or information with a higher incentive than the given

range and that ought to be beneath the edge recurrence. The measure of these frequencies will

rely upon the structuring of filters. The bandpass filter forms the signs inside the cut off

frequency. It is joined between low pass and high pass methods. It is now and then alluded to

as the movement of the filter in that every recurrence is communicated in dB to weaken the

given recurrence with a specific slice off recurrence to accomplish the proposed plan of the

electronic bandpass filter.

In band-stop filter or the band-rejection filter are those filters which passes all frequency

signal and attenuates a range of frequency. Similar to this we have a notch filter. It also passes

all the frequency that is below the first cut-off frequency and above the second cut-off

frequency and attenuates the rest of the frequency range. The quality factor of the notch filter

is very high that is the stop band of it is narrow. It application is wide spread in many area

like it is used in Raman spectroscopy, live sound reproduction, in the instrument amplifier.

This filter is one of the best filters used for the noise reduction in audio signal. Its stopband

width is around 1 ton 2 decades. But in audio de-noising process the notch filter has the

stopband width that is only semitones apart.

The mathematical equation used for the transfer function the notch filter is:

( )



Here is the central rejected frequency and is the width of the band which is to be

rejected.

Elliptical Filter is a type of radio frequency filter which provides a very fast transition

from pass-band to finally roll-off rate. In this filter there are equal ripple in both passband and

stopband and one of the advantage of this filter is that the ripple in it can be controlled in very

efficient manner[5,6]. No other filter have fast transition of gain from passband to the

stopband. When we adjust the ripple in the passband as zero then it will act like a Chebyshev

filter of type II. And by removing the ripple in stopband we get the Chebyshev filter of type I.

finally by removing ripple from both passband and stopband we get the Butterworth filter. It

can designed as low pass, high pass or band-stop filter. In audio signal de-noising, we mainly

use the band-stop filter.An elliptic filter is for the most part indicated by requiring a specific

incentive for the passband ripple, stopband swell and the sharpness of the cutoff. This will by

and large determine a base estimation of the filter arrange which must be utilized. Another

plan thought is the affectability of the gain capacity to the estimations of the electronic parts

used to manufacture the channel. This affectability is contrarily corresponding to the quality

factor of the posts of the exchange capacity of the filters.The combined signal generated as

shown in Fig. 2 is filtered by passing it through the designed filters. The design procedure is

carried on the block diagram used in the filtering process in Fig. 3. We can see that first we

design the notch filter and then we used the elliptical filter to separate the noise from the noisy

signal.

Figure 2 Block diagram of the combination of the signal.

Figure 3 Block diagram of the step by step processing through filter.



In the design of Notch Filter we mainly focus on designing of filter at frequency near by

the frequency of noise signal. So, first of all we design a series of notch frequency of different

cut-off frequency and of different width so as to cover the entire range of audio signal

frequency[7]. Used MATLAB in-built function to design the notch filter of different

frequencies. Here used sampling rate at 44100 and notch width is equal to 0.1.These are the

details of the notch filter used:

Table 2 Notch frequency

Notch filter Notch frequency ( ) 1 1347.95

2 1011.75

3 2021.75

4 673.5

5 474.5

6 337

Figure 4 Notch Filter 6 with given cut-off frequency.





We analyze the spectrum of audio signal and then decide the frequency range of which we

have to design the filter[8,9]. Here we used an elliptical filter in-built function of the

MATLAB and selected the order of the filter as N=6. Then, in this filter we set the cut-off

frequency of the higher frequency till which we want to filter the signal and same for the

lower frequency. We got the cut-off frequency as and . Elliptical filter is also

known as Cauer filters[10]. The advantage of elliptical filter over other type of filters is that

this filter can meet the necessities with lowest order of any supported filter type.

4. RESULTS

As we know the final output is an audio signal which is free from noise signal so the train

signal and frequency representation is shown in Fig. 10 and Fig. 11. The frequency range of

the train signal is between . The audio signal is of the frequency . The audio signal and its frequency spectrum is shown Fig. 12 and Fig. 13.The output

of the combined spectrum is shown in the Fig. 14. The spectrum of the output signal from

notch filter is shown in Fig. 16. And the final spectrum of the filtered audio signal is shown in

Fig. 17.in the filtered output the amplitude of the audio signal is reduced, and it can be re-gain

by using amplifier of proper gain value. So, the output filtered signal is almost similar to the

audio signal we used in beginning. Here in the spectrum of the signal we get as an output

from the notch filter, does not contain the noise signal spikes as shown in Fig. 15.

Figure 10. Train Noise Signal spectrum



Figure 11 Fast Fourier Transform spectrum of Train signal.

Figure 12 Audio Signal graph.

Figure 13 Fast Fourier Transform spectrum of Audio signal.



Figure 14 Graph of the combination of both the audio and noise signal.

Figure 15 Fast Fourier Transform spectrum of combined signal.

Figure 16 Spectrum of the signal from the notch filter.



Figure 17 Spectrum of the Filtered audio signal

5. CONCLUSIONS

The above methodology of de-noising audio signal is implemented in MATLAB 16. The

required objective of the project has been feasibly achieved and the speech signal has been

successfully separated from the train corrupted noise signal. And the notch filter is very

efficient in removing the noise signal of a particular frequency. As a future enhancement,

techniques may be used to enhance the quality of the output audio speech signal for better

clarity and effect. As we used different filters of different cut-off frequency to remove noise

from the audio signal, the filtered signal is close to the original audio signal. The notch and

elliptical filters are very effective in removing noise signal. In this project we focused on the

train signal as a noise signal.

ACKNOWLEDGEMENT

The author wish to thank Prof. Ravi Kumar C.V. This work was supported in part by a grant

from Prof. Ravi Kumar C.V.

REFERENCES

[1] Rajat Jain, Deepti Gupta, Faisal Ali and Alka Agarwal (2016), “Design and analysis of

low pass elliptical filter” International Conference on Computational Intelligence &

Communication Technology (CICT).

[2] S. Mitra, K. Hirano, S. Nishimura (1974), “Design of digital Notch filters” IEEE

Transactions on Communications, Volume 22, issue 7.

[3] Gang li, (1997), “A stable and efficient adaptive notch filter for direct frequency

spectrum” IEEE Transactions on Signal Processing, Volume 45, issue 8.

[4] A. La. Neve, F. Leonardi (2004), “Digital Signal processing with MatLab and DSP kits”

3rd

IEEE Signal Processing Education Workshop. 2004 IEEE 11th Digital Signal

processing workshop.

[5] C.V. Ravikumar, Saranya KC, (2016),” Implementing Mobile adhoc Networks with

improved AODV protocol” International Journal of Applied Engineering and Research, Vol

11,No. 9, pp. 6284-6289

[6] C.V. Ravikumar, Kala Praveen Bagadi, (2017),” Receiver design using artificial Neural

Network for signal detection in MC-CDMA system.” International Journal of Intelligent

Engineering & Systems”.



[7] C.V. Ravikumar, Kala Praveen Bagadi, (2016),” Robust Neural network based multiuser

detector in MC-CDMA mMAI mitigation.” Indian Journal of Science and Technology, Vol.

9 issue 30 .95994.

[8] Kanaparthy Rama Bramham & Ravi Kumar CV. (2015), “Comparison and Optimization of

Layer2 and Multilayer switch protocols to implement converged and reliable network.”

International Journal of Applied Engineering and Research, Vol 10,No. 8, pp. 20139-20154.

[9] C.V. Ravikumar, Kala Praveen Bagadi, (2016), “Performance analysis of HSRP in

provisioning Layer-3 Gateway Redundancy for Corporate Networks.” Indian Journal of

Science and Technology, Vol. 9 issue 20 .89851

[10] C.V.Ravi kumar, Kala Praveen Bagadi, (2016), “Performance analysis of IPv4 to IPv6

Transition Methods.” Indian Journal of Science and Technology, Vol .9 issue 20,90005

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