A Noise Reduction Method Based on Modified Least Mean Square Algorithm of Real Time Speech Signals With The Help of Wiener Filter

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 757
A Noise Reduction Method Based on Modified Least Mean Square
Algorithm of Real Time Speech Signals With The Help of Wiener Filter
1Dr S.China Venkateswarlu,2Srijan Verma, 3Kakasani Sai Teja, 4Chintha Sudheer
1Professor, Institute of Aeronautical Engineering, Hyderabad, Telangana
234Students of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad,
Telangana
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Abstract - Real-time voice denoising employs an
adaptive filtering technique with variable length filters
that tracks the noise characteristics and selects the filter
equations based on those features. The LMS algorithm's
primary benefits are its low computational complexity and
evidence of convergence in stationary environments. This
research proposes a modified LMS technique for real-time
speech signal denoise. The suggested approach increases
the capabilities of adaptive filtering by fusing the general
LMS algorithm with the diffusion least mean-square
algorithm. The suggested algorithm is successful in
reducing speech signal noise, according to the calculation
of the performance parameter. For replications and
additional research applications, a complete MATLAB
programming method is given.
Key Words: Speech Enhancement, LMS, MATLAB,
Modified LMS Algorithm, Segmental SNR, LLR, ISD,
Cepstrum, Weiner Filter.
1.INTRODUCTION
Voice transmissions may experience interference from
various noise components while being transported via
transmission lines before they reach their destinations. If
these noise components are not eliminated, the voice
signals may degrade to the point where their receiving
ends suffer a partial or complete loss of the information
content. The elimination of these undesirable
components has been addressed by numerous
researchers using various adaptive filtering techniques.
In order to minimise noise in speech signals, the
authors showed how well the recursive least square
(RLS) algorithm performed. They got three noise
components machine gun, F16, and speech noises from
the NOISE-92 database in addition to a clean voice signal
from the Hindi speech database. The sampling frequency
and resolution for both the noise and the clean signal
components are 16 KHz and 16 bits, respectively. Twelve
separate noisy speech signals were created by
successively adding each of the three noises to a clean
speech signal at signal to noise ratios (SNR) of -5dB, 0dB,
5dB, and 10dB levels. Six specially created filters were
each fed a set of noisy signals in order to simulate them.
The non-variable forgetting factor RLS (NVFFRLS), often
known as the RLS algorithm, powers two of the filters,
one of order 5 and the other of order 10, while the other
two filters, both of order 2, are powered by variable
forgetting factor RLS (VFFRLS) algorithm isused to drive
two of the orders, one of order 5, and two of order 10.
When it comes to RLS, the forgetting factor has a value of
=0.99 while it has a value of min=0.95 when it comes to
VFFRLS.
This is due to the fact that the VFFRLS algorithm can
monitor changes in the noisy signal more precisely than
the RLS method. The performance of the RLS algorithm
with a variable forgetting factor for non-stationary
processes has improved, which is consistent with the
researchers' findings. The researchers created a brand-
new adaptive filtering method called the modified
adaptive filtering with averaging (MAFA) algorithm,
which is utilized to remove white Gaussian noise from
voice samples.
1.1 BLOCK DIAGRAM AND FLOWCHART
In this approach, the algorithm's parameters are
improved by adding a weiner filter to the already-
existing algorithm. The parameters of the original
method and the suggested approach are compared after
the Weiner filter has been added. The NOIZEUS sound
database is used to source the noise signals. Signals with
different strengths, ranging from 0 dB to 15 dB,

Fig.1 Proposed Noise Algorithm
Are compared. Speech is analysed frame-by-frame under
the assumption that it is quasistationary. We assume
that the clean speech signal xt(n) is deteriorated by
additive noise vc(n) at each occurrence of time n, and the
noisy signal is obtained as yc(n).
In a model with additive noise,
yc(n)=xc(n)+vc(n)
Fig.2 Detailed Algorithm for LMS Denoising
1.2 IMPLEMENTATION
Speech is analysed frame-by-frame under the
assumption that it is quasistationary. We assume that
the clean speech signal xt(n) is deteriorated by additive
noise vt(n) at each occurrence of time n, and the noisy
signal is obtained as yt(n).
�c(�)=�c(�)+�c(�)Increase in Segmental SNR: In any
voice signal, energy change erratically and are not
stationary in nature. As a result, each frame segment is
computed independently and then added together to
generate segmental SNR in order to obtain an accurate
SNR value.
Thus, where N is the frame length, the equation for
segmented SNR is provided.
Original loud speech is represented by x(n). The signal of
the processed speech is x(n).
Log Likelihood Ratio (LLR): LLR determines the
amount of distortion added during processing by
comparing the spectrum of clean audio with processed
speech. Equation (4) displays the formula for calculating
LLR as DLLR
(ae,ac)=log10(aeRcaeT/acRcacT) (4)
where ac is the clean speech signal's LPC vector.
The LPC vector of the improved or processed speech
signal is called ae. The clean voice signal's
autocorrelation matrix is denoted by Rc.
Itakura-Saito Spectral Distance (ISD): This term refers
to the difference between enhanced and clean voice
signals in terms of the associated spectral envelope.
ISD's standard value is never greater than 100.
Cepstrum: For linear separation, homomorphic signals
incorporating convolution (such as a source and filter)
are converted into the sums of their cepstra using the
ceptrum representation. A common feature vector for
characterising the human voice and musical sounds is
the power cepstrum. The spectrum is typically initially
converted using the mel scale for these applications.

Database: The noise signals have been taken from
NOIZEUS database. 24 Noise signals have been taken
from the database (with the exception of train signals).
Each sentence is distorted by eight different types of
real-world noises (Restaurant, Station, Airport,babble,
Car, Street). The extracted noises segments are
artificially added to the filleted clean speech signal in
order to reach the desired SNR levels.
The noise signals from the NOIZEUS database are being
passed through the matlab algorithm and the
parameters have been verified. These parameters are
compared to the existing algorithm and the output signal
has been compared to the previous datasets.
Table. 1 Description of Speech Database
The results of the current experiment showed that the
suggested technique outperformed the LMS Algorithm in
terms of increasing the quality of the speech signal. This
was supported by the three objective measurements of
segmental SNR, LLR, Cepstrum, and ISD. Different levels
of noise (0, 5, 10, and 15 dB) are required for proper
analysis.
2. RESULTS AND DISCUSSIONS
The experimental findings of the current investigation
are presented in this section. To evaluate the
effectiveness of the suggested speech enhancement
method and compare the denoise speech signal, all
experiments were run using the NOIZEUS speech corpus
database. Many researchers have used this database for
applications involving voice improvement, and its
gender-matched database has 30 IEEE sentences. For
analysis purposes, one of the voice samples with a 10 dB
SNR is left out of this work(Train).All of the voice
samples in the database have this windowing applied to
them. Later, an additive noise with various SNRs and the
same original speech signal window length is introduced
to the speech samples. The STFT is then used to derive
the magnitude and phase spectrum values from the
noisy voice inputs.
Finally, a two-stage method is used to reconstruct the
speech signals in order to obtain the time domain speech
signals. The original (clean) speech signal and the
augmented speech signal are then calculated into four
different objective measures for performance evaluation
over each frame. The original (clean) and improved
speech signals are shown in a time series plot over four
different SNRs. The signal fluctuations over the course of
the 28064 samples are shown in this plot for various
SNRs.
The experimental findings of the current investigation
are presented in this section. To compare the denoise
speech signal and evaluate the effectiveness of the
proposed speech enhancement method, all tests were
run using the NOIZEUS speech corpus database. The
signal fluctuations over the course of the 28064 samples
are shown in this plot for various SNRs. The spectral
power distribution for a spoken signal at 0 dB, 5 dB, 10
dB, and 15 dB with airport noise.
Fig 3 . Street 15dB

Fig 4. Airport 5 Db Fig 5. Babble 10 Db
Fig 6. Restaurant 10 Db Fig 7. Car 0 Db
Fig 8. Station 0 Db

S. No. Noise Type Parameters
Noise Level
0 dB 5 dB 10 dB 15 dB
1 Airport
Seg SNR 3.24 2.194 1.8 1.69
Cepstrum 7.124 7.178 7.304 7.226
LLR 1.25 1.265 1.355 1.3154
ISD 88.06 99.83 99.99 99.92
2 Babble
Seg SNR 3.20 2.15 1.78 1.67
Cepstrum 7.2341 7.388 7.55 7.344
LLR 1.344 1.40 1.46 1.36
ISD 91.41 99.66 99.89 99.98
3 Restaurant
Seg SNR 3.24 2.14 1.78 1.66
Cepstrum 7.39 7.460 7.30 7.41
LLR 1.35 1.38 1.32 1.39
ISD 91.88 99.83 100 100
4 Station
Seg SNR 2.33 2.13 1.75 1.69
Cepstrum 7.19 7.38 7.47 7.39
LLR 1.30 1.36 1.41 1.38
ISD 94.45 99.81 100 100
5 Car
Seg SNR 3.03 1.92 1.74 1.67
Cepstrum 7.19 7.23 7.37 7.39
LLR 1.30 1.31 1.34 1.37
ISD 92.00 99.99 100 100
6 Street
Seg SNR 3.35 1.81 1.72 1.69
Cepstrum 6.96 7.58 7.35 7.21
LLR 1.17 1.47 1.35 1.30
ISD 90.20 100 100 99.97
Table. 2 Speech Enhancement Outputs

3. CONCLUSIONS
The modified LMS algorithm with the additional Weiner
filter is suggested in this thesis. The proposed algorithm
has produced satisfactory results when tested with
different noise levels. This method can be utilised in a
variety of settings, from the car to the airport, and is
effective at reducing different types of noise levels.
Through the objective measurements of Segmental SNR,
LLR, ISD, and Cepstrum, this experiment demonstrated
that the suggested method worked well in enhancing the
quality of the speech signal over LMS Algorithm. Future
work should concentrate on improving the current data
and testing the algorithm with different other parameters.
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A Noise Reduction Method Based on Modified Least Mean Square Algorithm of Real Time Speech Signals With The Help of Wiener Filter

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A Noise Reduction Method Based on Modified Least Mean Square Algorithm of Real Time Speech Signals With The Help of Wiener Filter