Investigation of broadcast-audio semantic analysis scenarios employing radio-programme-adaptive pattern classification
The present paper focuses on the investigation of various audio pattern classifiers in broadcast-audio semantic analysis, using radio-programme-adaptive classification strategies with supervised training. Multiple neural network topologies and training configurations are evaluated and compared in combination with feature-extraction, ranking and feature-selection procedures. Different pattern classification taxonomies are implemented, using programme-adapted multi-class definitions and hierarchical schemes. Hierarchical and hybrid classification taxonomies are deployed in speech analysis tasks, facilitating efficient speaker recognition/identification, speech/music discrimination, and generally speech/non-speech detection-segmentation. Exhaustive qualitative and quantitative evaluation is conducted, including indicative comparison with non-neural approaches. Hierarchical approaches offer classification-similarities for easy adaptation to generic radio-broadcast semantic analysis tasks. The proposed strategy exhibits increased efficiency in radio-programme content segmentation and classification, which is one of the most demanding audio semantics tasks. This strategy can be easily adapted in broader audio detection and classification problems, including additional real-world speech-communication demanding scenarios.
radio-programmes pattern classification neural networks audio-semantics content-management speech/non-speech segmentation