Environment-aware ideal binary mask estimation using monaural cues

We present a monaural approach to speech segregation that estimates the ideal binary mask (IBM) by combining amplitude modulation spectrogram (AMS) features, pitch-based features and speech presence probability (SPP) features derived from noise statistics. To maintain a high mask estimation accuracy in the presence of various background noises, the system employs environment-specific segregation models and automatically selects the appropriate model for a given input signal. Furthermore, instead of classifying each time-frequency (T-F) unit independently, the a posteriori probabilities of speech and noise presence are evaluated by considering adjacent T-F units. The proposed system achieves high classification accuracy.