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Audio Summarization with Audio Features and Probability Distribution Divergence

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Abstract

The automatic summarization of multimedia sources is an important task that facilitates the understanding of an individual by condensing the source while maintaining relevant information. In this paper we focus on audio summarization based on audio features and the probability of distribution divergence. Our method, based on an extractive summarization approach, aims to select the most relevant segments until a time threshold is reached. It takes into account the segment's length, position and informativeness value. Informativeness of each segment is obtained by mapping a set of audio features issued from its Mel-frequency Cepstral Coefficients and their corresponding Jensen-Shannon divergence score. Results over a multi-evaluator scheme shows that our approach provides understandable and informative summaries.
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Dates and versions

hal-02171387 , version 1 (02-07-2019)

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  • HAL Id : hal-02171387 , version 1

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Carlos-Emiliano González-Gallardo, Romain Deveaud, Eric Sanjuan, Juan-Manuel Torres-Moreno. Audio Summarization with Audio Features and Probability Distribution Divergence. 20th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France. ⟨hal-02171387⟩

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