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- W2302081516 abstract "The importance of detecting and characterizing sound events in the context of soundscape analysis is more and more understood among acousticians, architects and urban planners. A compilation of typical sounds for a given location, supplemented with a selection of rare but salient sound events, can quickly and efficiently provide an overview of the specific soundscape. The main subject of this paper is to construct a computational model to automatically create such an acoustic summary, using a biologically inspired auditory processing model. In particular, a neural network approach is taken, using a self-organizing map (also called a Kohonen map). A series of psychoacoustic features extracted from the sound forms the input data of the model at each time step. Typically, the learning algorithm of self-organizing maps is strictly dependent on the frequency of occurrence of the input data. However, human perception and retrospective assessment of soundscapes depend on more than only the rate of occurrence of particular sounds. For example, salient events will be remembered more vividly, even if they occur only rarely. To take into account this effect, a specific unsupervised continuous learning algorithm is used, which promotes memorizing less often occurring sounds too. At the end of training, the self-organized map represents an exhaustive acoustic summary of the soundscape at a particular location, and can be used to retrieve and listen to particular sound events." @default.
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- W2302081516 date "2011-01-01" @default.
- W2302081516 modified "2023-09-27" @default.
- W2302081516 title "Soundscape analysis by means of a neural network-based acoustic summary" @default.
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