Real time implementation of an adaptive filter for speech enhancement
Jolien D. E. Creighton, Rajamani Doraiswami · 2004
An adaptive linear prediction filter for speech enhancement is implemented in real time on a PC interfaced to an A/D and D/A converter board. A least means squares (LMS) algorithm is employed to update the filter weights where the learning factor is adaptively adjusted to provide faster convergence. The prediction horizon is chosen to be larger than the correlation length of the noise, thereby not restricting the noise to be white. The voice activity and noise segment of the speech waveform are detected by using the energy of the adaptive filter output. This is used to attenuate the noise-only portion. The proposed scheme is evaluated on number of speech samples.