Abstract-This article investigates a novel use of the wellknown audio words representations to detect specific audio events, namely gunshots and explosions, in order to get more robustness towards soundtrack variability in Hollywood movies. An audio stream is processed as a sequence of stationary segments. Each segment is described by one or several audio words obtained by applying product quantization to standard features. Such a representation using multiple audio words constructed via product quantisation is one of the novelties described in this work. Based on this representation, Bayesian networks are used to exploit the contextual information in order to detect audio events. Experiments are performed on a comprehensive set of 15 movies, made publicly available. Results are comparable to the state of the art results obtained on the same dataset but show increased robustness to decision thresholds, however limiting the range of possible operating points in some conditions. Late fusion provides a solution to this issue.
Furthermore, Penet et al. REF proposes a novel use of the well-known audio words representations by describing each segment by one or several audio words obtained by applying product quantization to standard features.
Audio event detection in movies using multiple audio words and contextual Bayesian networks
2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)
2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI)
Computer Science