The task of general audio detection and segmentation is quite common in contemporary audio applications where computationally intensive processes are frequently involved. Machine learning is usually employed along with user-enabled data labeling that is intended to detect, segment, and semantically annotate the relevant audio events. This work focuses on a generic audio detection and classification method that combines hierarchical bimodal segmentation with hybrid pattern classification at different temporal resolutions. This paper presents the algorithmic perspective of a mobile back-end system to facilitate the construction, validation, and continuous update of generic audio ground-truth data. The goal is the implementation of a system that is capable of performing well in different conditions without relying on complicated pattern recognition systems and taxonomies. For this reason, minimal prior knowledge is necessary so that there is consistent behavior for different input signals and computational environments. Novel “classification confidence” metrics are implemented.
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