Temporal ICA for classification of acoustic events i a kitchen environment

We describe a feature extraction method for general audio modeling using a temporal extension of Independent Component Analysis (ICA) and demonstrate its utility in the context of a sound classification task in a kitchen environment. Our approach accounts for temporal dependencies over multiple analysis frames much like the standard audio modeling technique of adding first and second temporal derivatives to the feature set. Using a real-world dataset of kitchen sounds, we show that our approach outperforms a canonical version of this standard front end, the mel-frequency cepstral coefficients (MFCCs), which has found successful application in automatic speech recognition tasks.