Methodological considerations concerning manual annotation of musical audio in function of algorithm development

In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1

Visual analysis for drum sequence transcription

A system is presented for analysing drum performance video sequences. A novel ellipse detection algorithm is introduced that automatically locates drum tops. This algorithm fits ellipses to edge clusters, and ranks them according to various fitness criteria. A background/foreground segmentation method is then used to extract the silhouette of the drummer and drum sticks. Coupled with a motion intensity feature, this allows for the detection of ‘hits’ in each of the extracted regions. In order to obtain a transcription of the performance, each of these regions is automatically labeled with the corresponding instrument class. A partial audio transcription and color cues are used to measure the compatibility between a region and its label, the Kuhn-Munkres algorithm is then employed to find the optimal labeling. Experimental results demonstrate the ability of visual analysis to enhance the performance of an audio drum transcription system