This paper presents an optimization, and a real-time implementation of a wavelet based speech compression system in STM32F4 discovery card. The optimization is done on the one hand by considering a Voice Activity Detection (VAD) to reduce the complexity and on the other hand by using a new quantization approach that codes each sample with fewer bits. The performance of the embedded audio codec is evaluated with a test technique called Processor-in-the-Loop (PIL) and using objective measures that can predict the perceived quality of the signal, namely SNR, PSNR and MSE. The compression efficiency is measured with the compression factor (CR). This research highlights the importance of the proposed optimizations. Indeed, they increase the CR without damaging the voice quality. The practical study shows that the proposed system meets the temporal and material requirements. Voice clarity is assessed with the Mean Opinion Score (MOS).
Abstract-S-transform is an effective time-frequency representation which gives simultaneous frequency and time distribution information alike the wavelet transforms (WT). However, the ST redundantly doubles the dimension of the original data set and the Discrete Orthonormal S-Transform (DOST) can decrease the redundancy of S-transform farther. So, this paper aims to propose a new method to remove additive background noise from noisy speech signal using DOST which supplies a multi-resolution analysis (MRA) spatial-frequency representation of image processing and signal analysis. Hence, the performances of the applied speech enhancement technique have been evaluated objectively and subjectively in comparison with respect to many other methods in four background noises at different SNR levels.
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