Classifying User Environment for Mobile Applications using Linear Autoencoding of Ambient Audio
R.G. Malkin, Alex Waibel · 2006
Many mobile devices and applications can act in context-sensitive ways, but rely on explicit human action for context awareness. It would be preferable if our devices were able to attain context awareness without human intervention. One important aspect of user context is environment. We present a novel method for classifying environment types based on acoustic signals. This method makes use of linear autoencoding neural networks, and is motivated by the observation that biological coding systems seem to be heavily influenced by the statistics of their environments. We show that the autoencoder method achieved a lower error rate than a standard Gaussian mixture model on a representative sample task, and that a linear combination of autoencoders and GMMs yielded better performance than either alone.