Blind channel estimation for audio signals
Stanley J. Wenndt, Andrew J. Noga · 2004
This research presents a new approach for blind channel estimation for audio signals. For most speech processing techniques such as speech recognition or speaker identification, the performance can drop significantly when the statistics of the training data such as the channel shape, noise, and distortion differ from the statistics of the testing data. Aside from the standard technique of cepstral mean normalization, few techniques are available for reducing channel mismatch conditions. Experimental results will be presented for both channel estimation and for channel normalization via inverse filtering where the inverse filter is derived from the channel estimate.