Audio Features Affected by Music Expressiveness
This work addresses the problem of understanding emotional expressiveness in music for Music Information Retrieval, but it appears incremental as it builds on existing feature analysis techniques without major breakthroughs.
The study investigated how a musician's intentional emotional expression affects audio features in tuba recordings, analyzing data from 10 players using statistical and machine learning methods, but no concrete numerical results were provided.
Within a Music Information Retrieval perspective, the goal of the study presented here is to investigate the impact on sound features of the musician's affective intention, namely when trying to intentionally convey emotional contents via expressiveness. A preliminary experiment has been performed involving $10$ tuba players. The recordings have been analysed by extracting a variety of features, which have been subsequently evaluated by combining both classic and machine learning statistical techniques. Results are reported and discussed.