Parallelization of Speech Compression Algorithm Based on Human Auditory System on Multicore System

Human auditory system has been successfully employed in speech compression to reduce bit rate requirement. Previous researches on speech compression stated that the use of simultaneous masking and/or temporal masking reduced the bit rate requirement while maintained perceptual quality. However, the benefit of using auditory masking in speech coding was outweighed by the amount of computation required to calculate masking threshold. Nevertheless, the current advances in microprocessor technology shows that to overcome heat generation and power cap, a multicore processor integrates two or more independent cores into a single package become popular. The objective of this research is to develop and implement a novel parallel speech compression algorithm based on human auditory system on a multicore system. To achieve a scalable parallel speech coding algorithm, Single Program Multiple Data (SPMD) programming model was used, in which a single program was written for all cores. Matlab parallel computing toolbox was used in the implementation. Finally, the performance of the developed parallel algorithm was evaluated using Perceptual Evaluation of Speech Quality (PESQ) and parallel execution time. Results show that the average PESQ score and pulse reduction for all 30 files when both auditory masking models were used is around 4.00 (transparent quality) and 41.12%, respectively. Moreover, the maximum speed achievable on our parallel experiment was around 2.45 if four cores were fully utilized.