A Real-Time Audio Compression Technique Based on Fast Wavelet Filtering and Encoding
Nella Romano, Antony Scivoletto, Dawid Połap
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 497–502 (2016)
Abstract. With the development of telecommunication technology over the last decades, the request for digital information compression has increased dramatically. In many applications, such as high quality audio transmission and storage, the target is to achieve audio and speech signal codings at the lowest possible data rates, in order to offer cheaper costs in terms of transmission and storage. Recently, compression techniques using wavelet transform have received great attention because of their promising compression ratio, signal to noise ratio, and flexibility in representing speech signals. In this paper we examine a new technique for analysing and compressing speech signals using biorthogonal wavelet filters. In particular, we compare this innovative compression method with a typical VoIP encoding of human voice, underlining how using wavelet filters may be convenient, mainly in terms of compression rate, without introducing a significant impairment in signal quality for listeners.
- D. Monro, “Audio compression,” August 2004, US Patent App. 10/473,649.
- O. O. Khalifa, S. H. Harding, and A.-H. Abdalla Hashim, “Compression using wavelet transform,” International Journal of Signal Processing, vol. 2, no. 5, pp. 17–26, 2008.
- H. Kaur and R. Kaur, “Speech compression and decompression using DWT and DCT,” Int. J. Computer Technology and Applications, vol. 3, no. 4, pp. 1501–1503, August 2012.
- F. D. Rango, M. Tropea, P. Fazio, and S. Marano, “Overview on VoIP: Subjective and Objective Measurement Methods,” International Journal of Computer Science and Network Security, vol. 6, no. 1, pp. 140–153, 2006.
- J. Davidson, Voice over IP fundamentals. Cisco press, 2006.
- [Online]. Available: http://www.ucci.it/docs/ICTSecurity-2003-18b
- M. Mehić, M. Mikulec, M. Voznak, and L. Kapicak, “Creating covert channel using sip,” in Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2014, pp. 182–192.
- S. Mallat, A wavelet tour of signal processing: the sparse way. Aca- demic press, 2008.
- D. L. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995.
- V. Malik, P. Singh, A. kumar Singh, and M. Singh, “Comparative analysis of Speech Compression on 8-bit and 16-bit data using different wavelets,” International Journal of Computer Trends and Technology (IJCTT), vol. 4, no. 5, May 2013.
- M. B. Abdallah, J. Malek, A. T. Azar, H. Belmabrouk, J. E. Monreal, and K. Krissian, “Adaptive noise-reducing anisotropic diffusion filter,” in Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2015, pp. 1–28.
- A. Scivoletto and N. Romano, “Performances of a parallel cuda program for a biorthogonal wavelet filter,” in Proceedings of the International Symposium for Young Scientists in Technology, Engineering and Math- ematics (System), 2016.
- J. Sanders and E. Kandrot, CUDA BY EXAMPLE, An Introduction to General-Purpose GPU Programming, NVIDIA.
- F. Bonanno, G. Capizzi, S. Coco, C. Napoli, A. Laudani, and G. Lo Sciuto, “Optimal thicknesses determination in a multilayer structure to improve the spp efficiency for photovoltaic devices by an hybrid femcascade neural network based approach,” in International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). IEEE, 2014, pp. 355–362.
- M. Wozniak, D. Polap, G. Borowik, and C. Napoli, “A first attempt to cloud-based user verification in distributed system,” in Asia-Pacific Conference on Computer Aided System Engineering (APCASE). IEEE, 2015, pp. 226–231.
- Z. Marszalek, M. Wozniak, G. Borowik, R. Wazirali, C. Napoli, G. Pappalardo, and E. Tramontana, “Benchmark tests on improved merge for big data processing,” in Computer Aided System Engineering (APCASE), 2015 Asia-Pacific Conference on. IEEE, 2015, pp. 96–101.
- K. Kaczmarski, M. Pilarski, B. Banasiak, and C. Kabut, “Content delivery network monitoring with limited resources,” in Federated Conference on Computer Science and Information Systems (FedCSIS), 2013. IEEE, 2013, pp. 801–805.
- D. Sinha and A. H. Tewfik, “Low Bit Rate Transparent Audio Compression using Adapted Wavelets,” IEEE Transactions On Signal Processing, vol. 41, no. 12, December 1993.
- M. Black and M. Zeytinoglu, “Computationally efficient wavelet packet coding of wide-band stereo audio signals,” in Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 5. IEEE, 1995, pp. 3075–3078.
- D. Sinha and J. D. Johnston, “Audio compression at low bit rates using a signal adaptive switched filterbank,” in Proceedings of International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2. IEEE, 1996, pp. 1053–1056.
- C. Napoli, G. Pappalardo, M. Tina, and E. Tramontana, “Cooperative strategy for optimal management of smart grids by wavelet rnns and cloud computing,” IEEE Transactions on Neural Networks and Learning Systems, (in press) 2015.
- C. Napoli, G. Pappalardo, E. Tramontana, R. Nowicki, J. Starczewski, and M. Woźniak, “Toward work groups classification based on probabilistic neural network approach,” in Proceedings of Artificial Intelligence and Soft Computing, ser. Lecture Notes in Computer Science, vol. 9119. Springer, 2015, pp. 79–89.
- C. Napoli, G. Pappalardo, and E. Tramontana, “A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over bittorrent,” International Journal of Applied Mathematics and Computer Science, vol. 26, no. 1, pp. 147–160, 2016.
- M. Wozniak, C. Napoli, E. Tramontana, and G. Capizzi, “A multiscale image compressor with rbfnn and discrete wavelet decomposition,” in International Joint Conference on Neural Networks (IJCNN). IEEE, 2015, pp. 1219–1225.
- C. Napoli and E. Tramontana, “An object-oriented neural network toolbox based on design patterns,” in Proceedings of the International Conference on Information and Software Technologies (ICIST), ser. CCIS, vol. 538. Springer, 2015, pp. 388–399.
- A. Calvagna and E. Tramontana, “Delivering dependable reusable components by expressing and enforcing design decisions,” in Proceedings of IEEE Computer Software and Applications Conference (COMPSAC) Workshop QUORS, Kyoto, Japan, July 2013, pp. 493–498.
- G. Capizzi, G. L. Sciuto, C. Napoli, E. Tramontana, and M. Woźniak, “A novel neural networks-based texture image processing algorithm for orange defects classification,” International Journal of Computer Science & Applications, vol. 13, no. 2, pp. 45–60, 2016.
- R. Giunta, G. Pappalardo, and E. Tramontana, “Superimposing roles for design patterns into application classes by means of aspects,” in Proceedings of ACM Symposium on Applied Computing (SAC). Riva del Garda, Italy: ACM, March 2012, pp. 1866–1868.
- L. Cui, S.-x. Wang, and T. Sun, “The application of wavelet analysis and audio compression technology in digital audio watermarking,” in Proceedings of International Conference on Neural Networks and Signal Processing, vol. 2. IEEE, 2003, pp. 1533–1537.
- P. Srinivasan and L. H. Jamieson, “High-Quality Audio Compression Using an Adaptive Wavelet Packet Decomposition and Psychoacoustic Modeling,” IEEE Transactions On Signal Processing, vol. 46, no. 4, April 1998.
- K. Dobson, N. Whitney, K. Smart, P. Rigstad, J. Yang et al., “Method and apparatus for wavelet based data compression having adaptive bit rate control for compression of digital audio or other sensory data,” oct 1998, US Patent 5,819,215.