This paper presents an effective technique for automatically clustering undocumented music recordings based on their associated singer. This serves as an indispensable step towards indexing and content-based information retrieval of music by singer. The proposed clustering system operates in an unsupervised manner, in which no prior information is available regarding the characteristics of singer voices, nor the population of singers. Methods are presented to separate vocal from non-vocal regions, to isolate the singers' vocal characteristics from the background music, to compare the similarity between singers' voices, and to determine the total number of unique singers from a collection of songs. Experimental evaluations conducted on a 200-track pop music database confirm the validity of the proposed system.
The relationships of Rn levels in basements and first floors of homes to topographic location in a northeastern ridge and valley section of Pennsylvania were investigated. Topographic variables relating to elevation and slope were quantified and related to house Rn levels using both conventional and classification and regression tree (CART) analyses. The original interest was in using topographic correlates to estimate missing values in epidemiologic studies. In this area, Rn levels are usefully predicted by relative and absolute elevations and nearness to hills and ridges. Three- to fourfold differences in geometric mean Rn levels can be obtained for categories representing significant portions of the population of houses. The relationship of first-floor to basement Rn levels was found to be curvilinear and modestly affected by topography.
This study presents an effective technique for automatically identifying the singer of a music recording. Since the vast majority of popular music contains background accompaniment during most or all vocal passages, directly acquiring isolated solo voice data for extracting the singer's vocal characteristics is usually infeasible. To eliminate the interference of background music for singer identification, we leverage statistical estimation of a piece's musical background to build a reliable model for the solo voice. Validity of the proposed singer identification system is confirmed via the experimental evaluations conducted on a 23-singer pop music database.
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