Higher Compression Rates for GSM 6.10 Standard Using Lossless Compression

Islam Younes Amro

Abstract


This research aims at exploiting the lossless Hamming correction code compression algorithm (HCDC) to reduce the transmission data rate in the GSM 6.10 standard, which holds several similarities with modern adaptive multi-rate codec in coefficients calculations and excitation principles. The compression algorithms depend on the properties of the hamming codes where data bits can be calculated from the parity bits. In this research, we chose parity equals 3 and data bits equals 4. Several iterations were conducted over the compressed frame information to achieve even higher compression rates. The compression rate was implemented over the standard of GSM 6.10, which is a variation Code Exited Linear Prediction coding (CELP). Regarding the data samples selected to conduct the test, two males and two females’ voice file samples at 8khz and quantized on 8-bit resolution were selected. The duration of the files varies from 4 to 6 seconds. Each sample was divided into 20ms frames; each frame was expressed using GSM6.10 with 260 bits of data included Linear perdition coefficients, pitch period, gain, peak magnitude value, grid position, and the sample amplitude. This shows that the 260 bits every 20ms form a data rate of 13kbps. The 260 bits were subjected to HCDC, and the data rate was reduced by 60%, reaching down to 5kbps on average. The results compared to the famous FLAC lossless audio compression, which showed 15% compression only. The research did not consider any quality testing since the compression is lossless. The research used standard ITU libraries to conduct the GSM6.10 data acquisition and open-source platforms for FLAC.


Keywords


Linear prediction coding, lossless compression, speech compression, source coding, cellular communication.

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References

- AbdulMuin Fathiah, & Gunawan Teddy, & Kartiwi Mira, & Elsheikh A. (2017). A review of lossless audio compression standards and algorithms. Held in Malesia and Published in AIP Conference Proceedings 1883, 020006

- Amro Islam, & Abu Zitar Raed, & Bahadili Al-Bahadili (2011). Speech compression exploiting linear prediction coefficients codebook and hamming correction code algorithm. Springer International Journal of Speech Technology volume 14, Article number: 65

- Amro Islam (2013). Higher Compression Rates for Code Excited Linear Prediction Coding Using Lossless Compression. Presented in the Fifth International IEEE Conference on Computational Intelligence, Communication Systems and Networks. Madrid: Publisher IEEE

- Bahadili Hussein (2008). A novel lossless data compression scheme based on the error correcting Hamming codes. Elsevier international journal Computers and Mathematics with Applications. 56:143-14

- Bhatty P. Ninad, & Kosta P., & Kosta P. (2011). Proposed modifications in ETSI GSM 06.10 full rate speech codec and its overall evaluation of performance using MATLAB. International Journal of Speech Technology 14(3):157-165

- Biesmans Wouter, & Das Neetha, & Francart Tom, & Bertrand Alexander (2017). Auditory-Inspired Speech Envelope Extraction Methods for Improved EEG-Based Auditory Attention Detection in a Cocktail Party Scenario. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 25(5):402-412

- Chu H. (2003). Speech Coding Algorithms. New York: Wiley.

- European Telecommunications Standards Institute ETSI (2010). Digital cellular telecommunications system (Phase 2+) and Universal Mobile Telecommunications System (UMTS)and LTE and Network architecture V9.3.0. FRANCE:650 Route des Lucioles F-06921 Sophia Antipolis Cedex

- GSM Association (2020). Adaptive Multirate Wide Band Version 5.0. GSM Association Publications.

- HU YI, & Philipos C. Loizu (2007). Evaluation of Objective Quality Measures for Speech Enhancement. IEEE Transactions on Audio, Speech, and Language Processing 16(1):229-238

- Kain A., & Macon M. (2001). Design And Evaluation of A Voice Conversion Algorithm Based On Spectral Envelope Mapping And Residual Prediction. Centre For Spoken Language Understanding (CSLU), Oregon Graduate Institute, OR 97006, USA. 2000

- LAM Y., & Goodman J. (2000). A mathematical analysis of the DCT coefficient distributions for images. IEEE Transactions on Image Processing 9(10):1661-1666

- Lin Xiao, & Li Gang, & Li Zhengguo, & Chia Thien King, & Yoh Ai Ling (2001). A novel prediction scheme for lossless compression of audio waveform. Presented in IEEE International Conference on Multimedia and Expo: Publisher IEEE, Japan

- Luo Da, & Rui Yang, & Bin Li, & Jiwu Huang (2017). Detection of Double Compressed AMR Audio Using Stacked Autoencoder. IEEE Transactions on Information Forensics and Security. 12(2):432-444

- Malvar Henrique (2007). Lossless and Near-Lossless Audio Compression Using Integer-Reversible Modulated Lapped Transforms. Presented in Data Compression Conference DCC07: Publisher IEEE, USA

- Menzies Dylan, & Filippo Maria Fazi (2017). Decoding and Compression of Channel and Scene Objects for Spatial Audio. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 25(11):2138-2151

- Openhaim et al. (1997). Signals and Systems. Second edition. NJ, USA: Prentice-Hall.

- Rao A.V., & Ahmadi S., & Linden J., & Gersho A. (2003). Pitch adaptive windows for improved excitation coding in low-rate CELP coders. IEEE Transactions on Speech and Audio Processing 11(6): 648-659

- Takehiro Sugimoto, & Shuichi Aoki, &Tomomi Hasegawa, & Tomoyasu Komori (2019). Advancement of 22.2 Multichannel Sound Broadcasting Based on MPEG-H 3-D Audio. IEEE Transactions on Broadcasting, PP (99):1




DOI: http://dx.doi.org/10.33977/2106-000-005-001

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