Real-time Performance Evaluation of Modified Cascaded Median-based Noise Estimation for Speech Enhancement System

Abstract

In this paper, the performance evaluation of Modified Cascaded Median (MCM)-based noise estimation method for speech enhancement system has been carried out. The MCM-based method, though reported earlier, was not extensively evaluated; particularly, its real-time performance had not been considered. In the present study, the performance of the MCM-based noise estimation method has been compared with those based on Dynamic Quantile Tracking (DQT) and Cascaded Median (CM), through simulation as well as real-time implementation using TMS320C6416T DSK. All comparisons were made for speech quality (subjectively — mean opinion score and objectively — PESQ score, log-likelihood ratio, weighted spectral slope distance, segmented signal-to-noise ratio and composite measures for signal distortion [Formula: see text], background intrusiveness [Formula: see text] and overall distortion [Formula: see text]) at the 95% level of confidence. The real-time parameters such as memory consumption and execution time have been used for real-time implementation and compared for the three methods. The results, for different SNR-based degraded speech signals, show that the modified cascaded median-based noise estimation is the best in terms of PESQ score, [Formula: see text], [Formula: see text], [Formula: see text] and mean opinion score. On the other hand, for different noise corrupted-based speech signals, it performs well as compared to the original CM. Memory consumption and average execution time for the MCM-based noise estimation lie in-between those for DQT and CM-based methods.