Degradation Type Classifier for Full Band Speech Contaminated With Echo, Broadband Noise, and Reverberation

This paper addresses the problem of identifying impairment types that might be present in a speech signal. In particular, three acoustically induced degradation types that occur in teleconference systems are considered: acoustic echo, reverberation, and broadband noise, as well as combinations among them. The proposed system is double-ended (full reference) and is developed using a database of degraded full-band speech signals created according to a model for teleconference systems. A set of features obtained from both the degraded and non-degraded signals is proposed and shown to adequately capture information associated with each degradation type. A random forest classifier and a support vector machine are successfully employed, achieving a classification error below 2%. Such classifiers can be used to select an appropriate quality assessment tool for a given degraded signal.