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Communication Dans Un Congrès Année : 2009

Factor Analysis for Audio-based Video Genre Classification

Driss Matrouf
Georges Linarès

Résumé

Statistical classifiers operate on features that generally include both useful and useless information. These two types of information are difficult to separate in the feature domain. Recently, a new paradigm based on a Latent Factor Analysis (LFA) proposed a model decomposition into usefull and useless components. This method was successfully applied to speaker and language recognition tasks. In this paper, we study the use of LFA for video genre classification by using only the audio channel. We propose a classification method based on short-term cep-stral features and Gaussian Mixture Models (GMM) or Support Vector Machine (SVM) classifiers, that are combined with Factor Analysis (FA). Experiments are conducted on a corpus composed of 5 types of video (musics, commercials, cartoons, movies and news). The relative classification error reduction obtained by using the best factor analysis configuration with respect to the baseline system, Gaussian Mixture Model Universal Background Model (GMM-UBM), is about 56%, corresponding to a correct identification rate of about 90%.
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Dates et versions

hal-01320228 , version 1 (23-05-2016)

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  • HAL Id : hal-01320228 , version 1

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Mickael Rouvier, Driss Matrouf, Georges Linarès. Factor Analysis for Audio-based Video Genre Classification. INTERSPEECH, Sep 2009, Brighton, United Kingdom. ⟨hal-01320228⟩

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