Single Channel Music Sound Separation Based on Spectrogram Decomposition and Note Classification

Separating multiple music sources from a single channel mixture is a challenging problem. We present a new approach to this problem based on non-negative matrix factorization (NMF) and note classification, assuming that the instruments used to play the sound signals are known a priori. The spectrogram of the mixture signal is first decomposed into building components (musical notes) using an NMF algorithm. The Mel frequency cepstrum coefficients (MFCCs) of both the decomposed components and the signals in the training dataset are extracted. The mean squared errors (MSEs) between the MFCC feature space of the decomposed music component and those of the training signals are used as the similarity measures for the decomposed music notes. The notes are then labelled to the corresponding type of instruments by the K nearest neighbors (K-NN) classification algorithm based on the MSEs. Finally, the source signals are reconstructed from the classified notes and the weighting matrices obtained from the NMF algorithm. Simulations are provided to show the performance of the proposed system. © 2011 Springer-Verlag Berlin Heidelberg

MCMC implementation for Bayesian hidden semi-Markov models with illustrative applications

Copyright © Springer 2013. The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-013-9399-zHidden Markov models (HMMs) are flexible, well established models useful in a diverse range of applications. However, one potential limitation of such models lies in their inability to explicitly structure the holding times of each hidden state. Hidden semi-Markov models (HSMMs) are more useful in the latter respect as they incorporate additional temporal structure by explicit modelling of the holding times. However, HSMMs have generally received less attention in the literature, mainly due to their intensive computational requirements. Here a Bayesian implementation of HSMMs is presented. Recursive algorithms are proposed in conjunction with Metropolis-Hastings in such a way as to avoid sampling from the distribution of the hidden state sequence in the MCMC sampler. This provides a computationally tractable estimation framework for HSMMs avoiding the limitations associated with the conventional EM algorithm regarding model flexibility. Performance of the proposed implementation is demonstrated through simulation experiments as well as an illustrative application relating to recurrent failures in a network of underground water pipes where random effects are also included into the HSMM to allow for pipe heterogeneity

Simulated annealing : a pedestrian review of the theory and some applications

Simulated annealing is a combinatorial optimization method based on randomization techniques. The method originates from the analogy between the annealing of solids, as described by the theory of statistical physics, and the optimization of large combinatorial problems. Here we review the basic theory of simulated annealing and recite a number of applications of the method. The theoretical review includes concepts of the theory of homogeneous and inhomogeneous Markov chains, an analysis of the asymptotic convergence of the algorithm, and a discussion of the finite-time behaviour. The list of applications includes combinatorial optimization problems related to VLSI design, image processing, code design and artificial intelligence

Mechanical properties of carbon nanotubes and nanofibers

Carbon nanotubes (CNTs) have extraordinary electrical and mechanical properties, and many potential applications have been proposed, ranging from nanoscale devices to reinforcement of macroscopic structures. However, due to their small sizes, characterization of their mechanical properties and deformation behaviours are major challenges. Theoretical modelling of deformation behaviours has shown that multi-walled carbon nanotubes (MWCNTs) can develop ripples in the walls on the contracted side when bent above a critical curvature. The rippling is reversible and accompanied by a reduction in the bending stiffness of the tubes. This behaviour will have implications for future nanoelectromechanical systems (NEMS). Although rippling has been thoroughly modelled there has been a lack of experimental data thus far. In this study, force measurements have been performed on individual MWCNTs and vertically aligned carbon nanofibers (VACNFs). This was accomplished by using a custom-made atomic force microscope (AFM) inside a scanning electron microscope (SEM). The measurements were done by bending free-standing MWCNTs/VACNFs with the AFM sensor in a cantilever-to-cantilever fashion, providing force-displacement curves. From such curves and the MWCNT/VACNF dimensions, measured from SEM-images, the critical strain for the very onset of rippling and the Young’s modulus, E, could be obtained. To enable accurate estimations of the nanotube diameter, we have developed a model of the SEM-image formation, such that intrinsic diameters can be retrieved. We have found an increase in the critical strain for smaller diameter tubes, a behaviour that compares well with previous theoretical modelling. VACNFs behaved very differently, as they did not display any rippling and had low bending stiffnesses due to inter-wall shear. We believe that our findings will have implications for the design of future NEMS devices that employ MWCNTs and VACNFs.Artikel 2 Image formation mechanisms tidigare som manuskript, nu publicerad: urn:nbn:se:kau:diva-16425 (MÅ 150924)</p