Pattern classification and audiovisual content management techniques using hybrid expert systems: A video-assisted bioacoustics application in Abdominal Sounds pattern analysis

The current paper focuses on the implementation of hybrid expert systems for audiovisual content description and management, by means of pattern analysis. The proposed methodology combines audio detection–segmentation, surveillance-video motion-detection and hierarchical audio pattern recognition, using neural networks, statistical clustering and syntactic pattern classification. The associated, video-assisted, bioacoustics application focuses on Abdominal Sounds (AS) pattern classification, promising to deliver new potentials in non-invasive Gastro-Intestinal Motility (GIM) monitoring. The current work introduces new techniques for content analysis automation of prolonged multi-channel recordings, facilitating automated GIM auscultation analysis. Thus, it seeks for the establishment of novel diagnostic tools over functional GIM disorders, with the advantage that subjects’ behavior and related psycho-physiological issues can be monitored to further analyze their dependencies. Qualitative and quantitative analysis validated the soundness of the adopted pattern classification taxonomy, resulting remarkable pattern recognition accuracy. Based on preliminary results, the proposed methodology can be successfully applied to general audiovisual content classification, description and management tasks (besides bioacoustics).