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Blind Channel Magnitude Response Estimation in Speech Using Spectrum Classification
IEEE Transactions on Audio, Speech, and Language Processing
We propose an algorithm for blind estimation of the magnitude response of a channel using the observations of a single microphone. The algorithm employs channel robust RASTA filtered Mel-frequency cepstral coefficients as features and a Gaussian mixture model based classifier to generate a dictionary of average speech spectra. These are then used to infer the channel response from speech that has undergone spectral modification in the capturing process. Simulation results using babble noise,doi:10.1109/tasl.2013.2270406 fatcat:5fjmbqb6g5e3bexbgumnr5lpte