MULTI-INSTRUMENT DETECTION IN POLYPHONIC MUSIC USING GAUSSIAN MIXTURE BASED FACTORIAL HMM

We formulate the problem of detecting the constituent instruments in a polyphonic music piece as a joint decoding problem. From monophonic data, parametric Gaussian Mixture Hidden Markov Models (GM-HMM) are obtained for each instrument. We propose a method to use the above models in a factorial framework, termed as Factorial GM-HMM (F-GM-HMM). The states are jointly inferred to explain the evolution of each instrument in the mixture observation sequence. The dependencies are decoupled using variational inference technique. We show that the joint time evolution of all instruments' states can be captured using F-GM-HMM. We compare performance of proposed method with that of Student's-t mixture model (tMM) and GM-HMM in an existing latent variable framework. Experiments on two to five polyphony with 8 instrument models trained on the RWC dataset, tested on RWC and TRIOS datasets show that F-GM-HMM gives an advantage over the other considered models in segments containing co-occurring instruments.

The Secreted Protein Rv1860 of Mycobacterium tuberculosis Stimulates Human Polyfunctional CD8(+) T Cells

We previously reported that Rv1860 protein from Mycobacterium tuberculosis stimulated CD4(+) and CD8(+) T cells secreting gamma interferon (IFN-gamma) in healthy purified protein derivative (PPD)-positive individuals and protected guinea pigs immunized with a DNA vaccine and a recombinant poxvirus expressing Rv1860 from a challenge with virulent M. tuberculosis. We now show Rv1860-specific polyfunctional T (PFT) cell responses in the blood of healthy latently M. tuberculosis-infected individuals dominated by CD8(+) T cells, using a panel of 32 overlapping peptides spanning the length of Rv1860. Multiple subsets of CD8(+) PFT cells were significantly more numerous in healthy latently infected volunteers (HV) than in tuberculosis (TB) patients (PAT). The responses of peripheral blood mononuclear cells (PBMC) from PAT to the peptides of Rv1860 were dominated by tumor necrosis factor alpha (TNF-alpha) and interleukin-10 (IL-10) secretions, the former coming predominantly from non-T cell sources. Notably, the pattern of the T cell response to Rv1860 was distinctly different from those of the widely studied M. tuberculosis antigens ESAT-6, CFP-10, Ag85A, and Ag85B, which elicited CD4(+) T cell-dominated responses as previously reported in other cohorts. We further identified a peptide spanning amino acids 21 to 39 of the Rv1860 protein with the potential to distinguish latent TB infection from disease due to its ability to stimulate differential cytokine signatures in HV and PAT. We suggest that a TB vaccine carrying these and other CD8(+) T-cell-stimulating antigens has the potential to prevent progression of latent M. tuberculosis infection to TB disease.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.