Speech enhancement using extreme learning machines
Babafemi O. Odelowo, David V. Anderson · 2017
The enhancement of speech degraded with the non-stationary noise types that typify real-world conditions has remained a challenging problem for several decades. However, recent use of data driven methods for this task has brought great performance improvements. In this paper, we develop a speech enhancement framework based on the extreme learning machine. Experimental results show that the proposed framework is effective in suppressing additive noise. Furthermore, it is always superior to a leading minimum mean square error (MMSE) algorithm in matched noise, and exceeds the said algorithm's performance in mismatched noise at all but the highest signal-to-noise ratio (SNR) tested.