Audio Summarization with Audio Features and Probability Distribution Divergence
This work addresses audio summarization for multimedia understanding, but it is incremental as it builds on existing extractive methods with specific feature enhancements.
The paper tackled audio summarization by selecting relevant segments based on audio features and Jensen-Shannon divergence, achieving understandable and informative summaries as evaluated by multiple assessors.
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.