Abstract
Once an audio recognition system that functions under idealistic conditions is established, the primary concern shifts towards making it robust in the real-world. Several options exist for system improvement along the chain of processing, and have proved to be promising especially in the monaural case. Here, most frequently methods and some recent candidates are explained, first including advanced front-end feature extraction, unsupervised spectral subtraction, feature enhancement and normalisation by Cepstral Mean Subtraction, Mean and Variance Normalisation, and Histogram Equalisation. Then, model-based feature enhancement based on (switching) linear dynamical modelling is followed by model architectures such as (hidden) conditional random fields, and switching autoregressive approaches. KeywordsAudio SignalConditional Random FieldWiener FilterFeature EnhancementAudio FeatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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