Using the recently proposed framework for latent perceptual indexing of audio clips, we present classification of whole clips categorized by two schemes: high-level semantic labels and the mid-level perceptually motivated onomatopoeia labels. First, feature-vectors extracted from the clips in the database are grouped into reference clusters using an unsupervised clustering technique. A unit-document co-occurrence matrix is then obtained by quantizing the feature-vectors extracted from the audio clips into the reference clusters. The audio clips are then mapped to a latent perceptual space by the reduced rank approximation of this matrix. The classification experiments are performed in this representation space using corresponding semantic and onomatopoeic labels of the clips. Using the proposed method, classification accuracy of about sixty percent was obtained when tested on the BBC sound effects library using over twenty categories. Having the two labeling schemes together in a single framework makes the classification system more flexible as each scheme addresses the limitation of the other. These aspects are the main motivation of the work presented here.
['Shiva Sundaram', 'Shrikanth Narayanan']
Classification of sound clips by two schemes: Using onomatopoeia and semantic labels