주파수 영역 블록 LMS 알고리즘을 이용한 잡음이 섞인 음성의 음질 개선

Abstract

In this paper, enhancement of speech corrupted by additive white or colored noise is studied. The unconstrained frequency-domain block least-mean-square (UFBLMS) adaptation algorithm and its frequency-weighted version are newly applied to speech enhancement, and their performances for enhancement are analyzed. For enhancement of speech degraded by white noise, the performance of the UFBLMS algorithm is superior to the spectral subtraction method or Wiener filtering technique by more than 3 dB in segmented frequency-weighted signal-to-noise ratio (FWSNR SEG)when SNR of speech is in the range of 0 to 10 dB.

As for enhancement of noisy speech corrupted by colored noise, the UFBLMS algorithm is superior to that of the spectral subtraction method by about 3 to 5 dB in FWSNR SEG. Also, it yields better performance by about 2 dB in FWSNR and FWSNR SEG than that of time-domain least-mean-square (TLMS) adaptive prediction filter (APF).

In view of the computational complexity and performance improvement in speech quality and intelligibility, the frequency-weighted UFBLMS algorithm appears to yield the best performance among various algorithms in enhancing noisy speech corrupted by white or colored noise.