1.
Content
• Abstract
• Introduction
• Literature Review

2.
Abstract:
• In today’s world multimedia files are used, storage space required for these files is more and sound files
have no option so ultimate solution for this is compression.
• Compression is nothing but high input stream of data converted into smaller size. Speech Compression is a
field of digital signal processing that focuses on reducing bit-rate of speech signals to enhance transmission
speed and storage requirement of fast developing multimedia.
• In many applications, such as the design of multimedia workstations and high quality audio transmission and
storage, the goal is to achieve transparent coding of audio and speech signals at the lowest possible data
rates. Therefore, the transmission and storage of information becomes costly. However, if we can use less
data, both transmission and storage become cheaper.
• Further reduction in bit rate is an attractive proposition in applications like remote broadcast lines, studio
links, satellite transmission of high quality audio and voice over internet. Different transforms such as
Discrete Wavelet Transform (DWT), Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT) are
exploited. A comparative study of performance of different transforms is made in terms of Signal-tonoise
ratio (SNR) and Peak signal-to-noise ratio (PSNR).

3.
Introduction
Speech is very basic way for humans to convey information. The main objective of Speech is
communication.
Speech can be defined as the response of vocal track to one or more excitation signal. Huge
amount of data transmission is very difficult both in terms of transmission and storage.
Speech Compression is a method to convert human speech into an encoded form in such a
way that it can later be decoded to get back the original signal. Compression is basically to
remove redundancy between neighboring samples and between adjacent cycles.
Major objective of speech compression is to represent signal with lesser number of bits.
The reduction of data should be done in such a way that there is acceptable loss of quality.

4.
S.No. Authors Volume/Issue No/Year Findings/Observations gap/Scope/Parameters
considered
1. Shahid Rahmani
Galgotias University,
Greater Noida
International Journal of
Electrical and Computer
Engineering (IJECE)
Vol.11,No.4,August 2021.
This paper compare basic audio
compression or techniques. which are
widely used in data compression its
hard to find it out which compression
technique should be used. Thus an
enhanced and properly implemented
lossless compression is used over the
lossy compression techniques
The Future scope of audio
compresion technique is to
compress reduces the
dynamic ranges of your
sound and audio
recording. Lowdown the
loudest part and make
them a peaceful volume.
2. J A Rolon-Heredia1 ,
V M Garrido-
Arevalo1 , and J
Marulanda2
978-1-7281-0211-5
Feb 2019
Therefore, this paper presents the
acquisition and digital processing of
voice signals, as well as the application
of the discrete cosine transform and
the wavelet transform using Matlab
software version 2017b, licensed by
the Technological University of Bolivar.
Literature Survey

5.
S.No. Author with
Affiliation
Volume/Issue No. Abstract/Findings Research
gap/Scope/Parameters
considered
3. Zainab T. DRWEESH,
Loay E.GEORGE
International Journal of
Electrical and Computer
Engineering (IJECE) Vol. 11,
No. 4, August 2021, pp.
3459~3469
In this paper, an efficient audio
compressive scheme is proposed, it
depends on combined transform coding
scheme; it is consist of, i) then the
produced sub-bands passed through
DCT to de-correlate the signal, the
product of the combined transform
stage is passed through progressive
hierarchical quantization.
The system can be
improved in the future
using audio fractal coding
as a compression tool
(instead of wavelet
transform coding and
DCT) in the compressive
audio scheme
4. Sankalp Shukla,
Maniram Ahirwar,
Ritu Gupta, Sarthak
Jain, Dheeraj Singh
Rajput
978-1-7281-0211-5
Feb 2019
This paper proposes a new approach to
Audio compression that incorporates
lossless text compression algorithm.
The purpose of Audio Compression is to
reduce the amount of data required to
represent the digital audio by removing
redundant data.
The existing MP3
compression uses
Modified Discrete Cosine
Transform and Audio
Masking while the
proposed algorithm as
major tools to reduce
audio file size. The
algorithm can be further
improved the techniques

6.
S.No. Author with
Affiliation
Volume/Issue No. Abstract/Findings Research
gap/Scope/Parameters
considered
5. M. V. Patil , Apoorva
Gupta , Ankita
Varma , Shikhar Salil
Vol.2,Issue 5,May 2013 In this paper a new lossy algorithm to
compress speech signal using discrete
wavelet transform (DWT) and then
again compressed by discrete cosine
transform (DCT) then decompressed it
by discrete cosine transform afterward
decompressed by discrete wavelet
transform to retrieve the original signal
in compressed form.
Experimental results show
that in general there is
improved in compression
factor & signal to noise
ratio with DWT based
technique. It is also
observed that Specific
wavelets have varying
effects on the speech
signal being represented
6. Mr. R. R. Karhe Ms.
P. B. Shinde Ms. J. N.
Fasale.
Vol.4 Issue 01,January-
2015
This paper describes the technique to
apply DCT and CS techniques to the
compression of audio signals. we can
treat audio signals as sparse signals in
the frequency domain.
This study represents a
DCT speech signal
representation has the
ability to pack input data
into as few coefficients as
possible. This allows
quantizes to discard
coefficients with relatively
small amplitudes without

7.
S.No. Author with
Affiliation
Volume/Issue No. Abstract/Findings Research
gap/Scope/Parameters
considered
7.
8.

8.
S.No. Author with
Affiliation
Volume/Issue No. Abstract/Findings Research
gap/Scope/Parameters
considered
9.
10.

9.
References:
[1] C. Zhang, W. Ahn, Y. Zhang, and B. R. Childers, “Live code update for IoT devices in energy harvesting environments,” 2016 5th
Non-Volatile Mem. Syst. Appl. Symp. NVMSA 2016, 2016, doi: 10.1109/NVMSA.2016.7547182.
[2] G. Manogaran, R. Varatharajan, D. Lopez, P. M. Kumar, R. Sundarasekar, and C. Thota, “A new architecture of Internet of Things
and big data ecosystem for secured smart healthcare monitoring and alerting system,” Futur. Gener. Comput. Syst., vol. 82, pp. 375–
387, 2018, doi: 10.1016/j.future.2017.10.045.
[3] F. Akhtar, M. H. Rehmani, and M. Reisslein, “White space: Definitional perspectives and their role in exploiting spectrum
opportunities,” Telecomm. Policy, vol. 40, no. 4, pp. 319–331, 2016, doi: 10.1016/j.telpol.2016.01.003.
[4] Y. Jararweh, M. Al-Ayyoub, A. Doulat, A. Al Abed Al Aziz, A. B. S. Haythem, and A. K. Abdallah, “Software defned cognitive radio
network framework: Design and evaluation,” Int. J. Grid High Perform. Comput., vol. 7, no. 1, pp. 15–31, 2015, doi:
10.4018/ijghpc.2015010102.
[5] K. Tang, W. Tang, E. Luo, Z. Tan, W. Meng, and L. Qi, “Secure Information Transmissions in Wireless-Powered Cognitive Radio
Networks for Internet of Medical Things,” Secur. Commun. Networks, vol. 2020, 2020, doi: 10.1155/2020/7542726.
[6] H. Chen, C. Zhai, Y. Li, and B. Vucetic, “Cooperative Strategies for Wireless-Powered Communications: An Overview,” IEEE Wirel.
Commun., vol. 25, no. 4, pp. 112–119, 2018, doi: 10.1109/MWC.2017.1700245.
[7] A. El Shafie, N. Al-Dhahir, and R. Hamila, “Cooperative access schemes for efficient SWIPT transmissions in cognitive radio
networks,” 2015 IEEE Globecom Work. GC Wkshps 2015 - Proc., 2015, doi: 10.1109/GLOCOMW.2015.7414050.
[8] A. Mukherjee, T. Acharya, and M. R. A. Khandaker, “Outage Analysis for SWIPT-Enabled Two-Way Cognitive,” vol. 67, no. 9, pp.
9032–9036, 2018.