Abstract
In the coarse-level segmentation and indexing stage, audio data are segmented and classified into basic audio types, based on morphological and statistical analysis of the temporal curves of the short-time energy function, the short-time average zero-crossing rate, and the short-time fundamental frequency, as well as the spectral peak tracks of audio signals. Threshold-based heuristical rules are derived empirically to guide the classification procedures. Therefore, the approach is completely generic and model-free, which can be applied under any circumstances. An illustration of the scheme is shown in Figure 4.1.
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© 2001 Springer Science+Business Media New York
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Zhang, T., Kuo, CC.J. (2001). Generic Audio Data Segmentation and Indexing. In: Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing. The Springer International Series in Engineering and Computer Science, vol 606. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3339-6_4
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DOI: https://doi.org/10.1007/978-1-4757-3339-6_4
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-4878-6
Online ISBN: 978-1-4757-3339-6
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