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Non-negative spectral factorisation has been used successfully for separation of speech and noise in automatic speech recognition, both in feature-enhancing front-ends and in direct classification. In this work, we propose employing spectro-temporal 2D filters to model dynamic properties of Mel-scale spectrogram patterns in addition to static magnitude features. The results are evaluated using an exemplar-based sparse classifier on the CHiME noisy speech database. After optimisation of staticdoi:10.1109/icassp.2012.6288823 dblp:conf/icassp/HurmalainenV12 fatcat:k5chrxkgkvdubotnpu72vzarsm