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- W2034138900 abstract "We present a descriptive approach for analyzing audio scenes that can comprise a mixture of audio sources. We apply this method to segment popular music songs into vocal and non-vocal sections. Unlike existing methods that directly rely on within-class feature similarities of acoustic sources, the proposed data-driven system is based on a training set where the acoustic sources are grouped by their perceptual or semantic attributes. Our audio analysis approach is based on a quantitative time-varying metric to measure the interaction between acoustic sources present in a scene developed using pattern recognition methods. Using the proposed system that is trained on a general sound effects library, we achieve less than ten percent vocal-section segmentation error and less than five percent false alarm rates when evaluated on a database of popular music recordings that spans four different genres (rock, hiphop, pop, and easy listening)" @default.
- W2034138900 created "2016-06-24" @default.
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- W2034138900 date "2006-10-01" @default.
- W2034138900 modified "2023-09-23" @default.
- W2034138900 title "An attribute-based approach to audio description applied to segmenting vocal sections in popular music songs" @default.
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- W2034138900 doi "https://doi.org/10.1109/mmsp.2006.285277" @default.
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