Contribution of Stereo Information to Feature-Based Pattern Classification for Audio Semantic Analysis

The current paper focuses on the evaluation of the contribution of stereo information to feature-based pattern classification for audio semantic analysis. In radio productions, where multiple speakers' voices and music coexist, it is quite important to detect voice and music patterns, in order to achieve efficient classification of the audio content. Recent research confirmed that the process of audio feature extraction and selection is critical for the overall system performance. In the current work, the feature extraction procedure is applied in each audio channel of stereo radio programmes separately in order to exploit the differences of audio information between channels. Feature ranking algorithms are employed, aiming to investigate the saliency of the feature differences. The classification performance of the implemented artificial neural systems revealed that the stereo differences have to be taken into consideration during audio semantic analysis.