New Speech Compression Technique based on Filter Bank Design and Psychoacoustic Model

In this paper, a new speech compression technique is proposed. This technique applies a Psychoacoustic Model and a general approach for Filter Bank Design using optimization. It is evaluated and compared with a compression technique using a MDCT (Modified Discrete Cosine Transform) Filter Bank of 32 Filters and a Psychoacoustic Model. This evaluation and comparison is performed by calculating bits before and after compression, PSNR (Peak Signal to Noise Ratio), NRMSE (Normalized Root Mean Square Error), SNR (Signal to Noise Ratio) and PESQ (Perceptual evaluation of speech quality) computations. The two techniques are tested and applied to a number of speech signals that are sampled at 8 kHz. The results obtained from this evaluation show that the proposed technique outperforms the second compression technique (based on a Psychoacoustic Model and MDCT filter Bank) in terms of Bits after compression and compression ratio. In fact, the proposed technique yields higher values for the compression ratio than the second compression technique. Moreover, the proposed compression technique presents reconstructed speech signals with acceptable perceptual qualities. This is justified by the values of SNR, PSNR and NRMSE and PESQ.