Perceptual segmentation and component selection for sinusoidal representations of audio

作     者：Painter, T Spanias, A 

作者机构：Intel Corp Handheld Comp Div Hudson MA 01749 USA Arizona State Univ Dept Elect Engn Tempe AZ 85287 USA 

出 版 物：《IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING》 (IEEE Trans Speech Audio Process)

年 卷 期：2005年第13卷第2期

页      面：149-162页

核心收录：

基　　金：Intel Research Council 

主　　题：audio coding psychoacoustics segmentation sinusoidal models 

摘      要：This paper presents two fundamental enhancements in a hybrid audio signal model consisting of sinusoidal, transient, and noise (STN) components. The first enhancement involves a novel application of a perceptual metric for optimal time segmentation for the analysis of transients. In particular, Moore and Glasberg s model of partial loudness is modified for use with general signals and then integrated into a novel time segmentation scheme. The second, and perhaps more significant STN enhancement is concerned with a new methodology for ranking and selection of the most perceptually relevant sinusoids. A systematic procedure is developed for the selection of a compact set of sinusoids and comparative results are given to demonstrate the merit of this method.