Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
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Due to the variability in characteristics of audio scenes, some can naturally be recognized earlier, i.e. after a shorter duration, than others. In this work, rather than using equal-length snippets for all scene categories, as is common in the literature, we study to which temporal extent an audio scene can be reliably recognized. For modelling, in addition to two single-network systems relying on a convolutional neural network and a recurrent neural network, we also investigate early fusion and late fusion of these two single networks for audio scene classification. Moreover, as model fusion is prevalent in audio scene classifiers, we further aim to study whether and when model fusion is really necessary for this task.
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