![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.](https://image.slidesharecdn.com/slideshare-230127160722-a08f0757/85/slideshare-pptx-9-320.jpg)
slideshare.pptx
- 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.