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Audio signal classification in reverberant environments based on fuzzy-clustered ad-hoc microphone arrays

Audio signal classification suffers from the mismatch of environmental conditions when training data is based on clean and anechoic signals and test data is distorted by reverberation and signals from other sources. In this contribution we analyze the classification performance for such a scenario with two concurrently active sources in a simulated reverberant environment. To obtain robust classification results, we exploit the spatial distribution of ad-hoc microphone arrays to capture the signals and extract cepstral features. Based on these features only, we use unsupervised fuzzy clustering to estimate clusters of microphones which are dominated by one of the sources. Finally, signal classification based on clean and anechoic training data is performed for each of the cluster. The probability of cluster membership for each microphone is provided by the fuzzy clustering algorithm and is used to compute a weighted average of the feature vectors. It is shown that the proposed method exceeds the performance of classification based on single microphones.