Abstract

The notion of acoustic stopwords is proposed to improve the performance of generic unstructured audio information retrieval systems. The rationale behind this is based on the assumption that not all portions of a generic audio signal contribute toward deriving specific descriptive categories (semantic words and onomatopoeias in this work). Detecting the non-salient regions in the audio can hence lead to more robust mapping of signal to categorical descriptions. Using the latent perceptual indexing (LPI) based framework, we propose to remove the proposed acoustic stopwords from the extracted acoustic features, which may include little information on descriptive categories. The acoustic stopwords are selected based on data-driven frequency-related measurements such as document frequency (DF) and inverse document frequency (IDF). The empirical results with BBC sound effect library show that removing …

Date

August 23, 2010

Authors

Samuel Kim, Shiva Sundaram, Panayiotis Georgiou, Shrikanth Narayanan

Conference

2010 18th European Signal Processing Conference

Pages

1277-1280

Publisher

IEEE