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Báo cáo hóa học: " Particle Filtering Applied to Musical Tempo Tracking Stephen W. Hainsworth"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Particle Filtering Applied to Musical Tempo Tracking Stephen W. Hainsworth | EURASIP Journal on Applied Signal Processing 2004 15 2385-2395 2004 Hindawi Publishing Corporation Particle Filtering Applied to Musical Tempo Tracking Stephen W. Hainsworth Department of Engineering University of Cambridge Cambridge CB2 1PZ UK Email swh21@cantab.net Malcolm D. Macleod QinetiQ Malvern WR14 3PS UK Email m.macleod@signal.qinetiq.com Received 30 May 2003 Revised 1 May 2004 This paper explores the use of particle filters for beat tracking in musical audio examples. The aim is to estimate the time-varying tempo process and to find the time locations of beats as defined by human perception. Two alternative algorithms are presented one which performs Rao-Blackwellisation to produce an almost deterministic formulation while the second is a formulation which models tempo as a Brownian motion process. The algorithms have been tested on a large and varied database of examples and results are comparable with the current state of the art. The deterministic algorithm gives the better performance of the two algorithms. Keywords and phrases beat tracking particle filters music. 1. INTRODUCTION Musical audio analysis has been a growing area for research over the last decade. One of the goals in the area is fully automated transcription of real polyphonic audio signals though this problem is currently only partially solved. More realistic sub-tasks in the overall problem exist and can be explored with greater success beat tracking is one of these and has many applications in its own right automatic accompaniment of solo performances 1 auto-DJs expressive rhythmic transformations 2 uses in database retrieval 3 metadata generation 4 etc. . This paper describes an investigation into beat tracking utilising particle filtering algorithms as a framework for sequential stochastic estimation where the state-space under consideration is a complex one and does not permit a closed form solution. Historically a number of methods have been used to attempt solution of the problem