Musical Parsing and Musical Affect
This paper explores the issue of what is going on in a listener9s mind during the real-time processing of music, such that it is possible to account for the listener9s understanding of the music. The issue will be approached through evidence internal to music itself, and also by analogy with evidence from the processing of language. I will then examine how processing of the sort I propose provides a basis for considering a particular issue in the theory of musical affect.
We focus on the problem of learning semantics from multimedia data associated with broadcast video documents. In this paper we propose to learn semantic concepts from multimodal sources based on style and context detectors, in combination with statistical classier ensembles. As a case study we present our method for detecting the concept of news subject monologues. This approach had the best average precision performance amongst 26 submissions in the 2003 video track of the Text Retrieval Conference benchmark. Experiments were conducted with respect to individual detector contribution, ensemble size, and ranking mechanism. It was found that the combination of detectors is decisive for the nal result, although some detectors might appear useless in isolation. Moreover, by using a probabilistic ranking, in combination with a large classier ensemble, results can be improved even further.