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: Research Article Drum Sound Detection in Polyphonic Music with Hidden Markov Models | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2009 Article ID 497292 9 pages doi 2009 497292 Research Article Drum Sound Detection in Polyphonic Music with Hidden Markov Models Jouni Paulus and Anssi Klapuri Department of Signal Processing Tampere University of Technology Korkeakoulunkatu 1 33720 Tampere Finland Correspondence should be addressed to Jouni Paulus Received 18 August 2009 Accepted 16 November 2009 Recommended by Richard Heusdens This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models HMMs . The task is to detect the temporal locations of unpitched percussive sounds such as bass drum or hi-hat and recognise the instruments played. Contrary to many earlier methods a separate sound event segmentation is not done but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs and that using detector-like models for each target drum provides a better performance than modelling drum combinations. Copyright 2009 J. Paulus and A. Klapuri. This is an open access article distributed under the Creative .