Comparison of wideband LMS, subband LMS, and a nonlinear entropic neural approach to adaptive noise cancellation for speech enhancement
An empirical comparison of three adaptive algorithms for speech enhancement in noisy reverberant conditions is presented. The subband least mean square (LMS) [E. Toner and D. R. Campbell, Speech Commun. 12, 253–259 (1993)] and minimum entropy noise reduction schemes [M. Girolami, Electron. Lett. 33(17), 1437–1438 (1997)] are compared with a reference wideband LMS approach [S. D. Stearns and R. A. David, Signal Processing Algorithms (Prentice–Hall, Englewood Cliffs, NJ, 1988)]. All three methods were applied to the enhancement of speech corrupted with speech‐shaped noise. The schemes were tested using simulated anechoic and real‐room reverberant (T60=0.3 s) environments. The anechoic and real‐room recordings were binaural including head shadow and diffraction effects. The subband LMS adaptive processing scheme uses the LMS adaptive noise cancellation algorithm in frequency‐limited subbands. The inputs from each microphone are split into 16 contiguous subbands using a cochlear distribution according the fun...