The predominant melodic source, frequently the singing voice, is an important component of musical signals. In this paper, we describe a method for extracting the predominant source and corresponding melody from ldquoreal-worldrdquo polyphonic music. The proposed method is inspired by ideas from computational auditory scene analysis. We formulate predominant melodic source tracking and formation as a graph partitioning problem and solve it using the normalized cut which is a global criterion for segmenting graphs that has been used in computer vision. Sinusoidal modeling is used as the underlying representation. A novel harmonicity cue which we term harmonically wrapped peak similarity is introduced. Experimental results supporting the use of this cue are presented. In addition, we show results for automatic melody extraction using the proposed approach.

Portable digital music players are becoming pervasive and the size of personal digital music collections has been steadily increasing (5-10 thousand tracks are common today). The emerging area of music information retrieval (MIR) deals with all aspects of managing, analyzing and organizing music in digital formats. The majority of work in MIR follows a search/retrieval paradigm. More recently, the importance of browsing as an interaction paradigm has been realized and several novel interfaces have been proposed. In this paper, we describe a tangible interface for content-aware browsing of music collections. The radio drum is a gestural interface based on capacitance sensors that can detect the x,y,z positions of two drum sticks in a 3D volume. We describe two possible mappings that can be used for browsing music collections without relying on metadata. The first is an explicit mapping of tempo and beat strength, and the second is a music similarity space using audio feature extraction and a self organizing map (SOM)