Automatic acoustic scene classification of real life, complex and unstructured acoustic scenes is a challenging task as the number of acoustic sources present in the audio stream are unknown and overlapping in time. In this work, we present a novel approach to classification such unstructured acoustic scenes. Motivated by the bottom-up attention model of the human auditory system, salient events of an audio clip are extracted in an unsupervised manner and presented to the classification system.

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... Similar to latent semantic indexing of text documents, the classification system uses unit-document frequency measure to index the clip in a continuous, latent space. This allows for developing a completely class-independent approach to audio classification. Our results on the BBC sound effects library indicates that using the saliency-driven attention selection approach presented in this paper, 17.5% relative improvement can be obtained in frame-based classification and 25% relative improvement can be obtained using the latent audio indexing approach.