With ever increasing amounts of radio broadcast material being made available as podcasts, sophisticated methods of enabling the listener to quickly locate material matching their own personal tastes become essential. Given the ability to segment a podcast which may be in the order of one or two hours duration into individual song previews, the time the listener spends searching for material of interest is minimised. This paper investigates the effectiveness of applying multiple feature extraction techniques to podcast segmentation, and describes how such techniques could be exploited by a vast number of digital media delivery platforms in a commercial cloud-based radio recommendation and summarisation service.
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