Paper Info
Title
Multiscale Fractal Analysis of Musical Instrument Signals With Application to Recognition
Abstract
In this paper, we explore nonlinear methods, inspired by the fractal theory for the analysis of the structure of music signals at multiple time scales, which is of importance both for their modeling and for their automatic computer-based recognition. We propose the multiscale fractal dimension (MFD) profile as a short-time descriptor, useful to quantify the multiscale complexity and fragmentation of the different states of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments, which is verified by further analysis on synthesized sinusoidal signals. We compare the descriptiveness of our features against that of Mel frequency cepstral coefficients (MFCCs), using both static and dynamic classifiers such as Gaussian mixture models (GMMs) and hidden Markov models (HMMs). The method and features proposed in this paper appear to be promising for music signal analysis, due to their capability for multiscale analysis of the signals and their applicability in recognition, as they accomplish an error reduction of up to 32%. These results are quite interesting and render the descriptor of direct applicability in large-scale music classification tasks.
Year
DOI
Venue
2013
10.1109/TASL.2012.2231073
Audio, Speech, and Language Processing, IEEE Transactions
Keywords
Field
DocType
Gaussian processes,fractals,hidden Markov models,musical instruments,signal classification,GMM,Gaussian mixture models,HMM,MFCC,MFD profile,Mel frequency cepstral coefficients,computer-based recognition,dynamic classifiers,hidden Markov models,multiple time scales,multiscale fractal analysis,multiscale fractal dimension,music classification tasks,music waveform,musical instrument signals,nonlinear methods,static classifiers,Fractals,multiscale analysis,music signals,timbre classification
Fractal analysis,Signal processing,Mel-frequency cepstrum,Fractal dimension,Pattern recognition,Computer science,Fractal,Speech recognition,Musical instrument,Artificial intelligence,Hidden Markov model,Mixture model
Journal
Volume
Issue
ISSN
21
4
1558-7916
Citations 
PageRank 
References 
5
0.44
7
Authors
2
Name
Order
Citations
PageRank
Athanasia Zlatintsi1464.49
Petros Maragos23733591.97