Audio-Based Objectionable Content Detection Using Discriminative Transforms of Time-Frequency Dynamics

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In this paper, the problem of detecting objectionable sounds, such as sexual screaming or moaning, to classify and block objectionable multimedia content is addressed. Objectionable sounds show distinctive characteristics, such as large temporal variations and fast spectral transitions, which are different from general audio signals, such as speech and music. To represent these characteristics, segment-based two-dimensional Mel-frequency cepstral coefficients and histograms of gradient directions are used as a feature set to characterize the time-frequency dynamics within a long-range segment of the target signal. After extracting the features, they are transformed to features with lower dimensions while preserving discriminative information using linear discriminant analysis based on a combination of global and local Fisher criteria. A Gaussian mixture model is adopted to statistically represent objectionable and non-objectionable sounds, and test sounds are classified by using a likelihood ratio test. Evaluation of the proposed feature extraction method on a database of several hundred objectionable and non-objectionable sound clips yielded precision/recall breakeven point of 91.25%, which is a promising performance which shows that the system can be applied to help an image-based approach to block such multimedia content.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2012-10
Language
English
Article Type
Article
Keywords

CLASSIFICATION

Citation

IEEE TRANSACTIONS ON MULTIMEDIA, v.14, no.5, pp.1390 - 1400

ISSN
1520-9210
DOI
10.1109/TMM.2012.2195481
URI
http://hdl.handle.net/10203/102477
Appears in Collection
EE-Journal Papers(저널논문)
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