DICTIONARY LEARNING OF CONVOLVED SIGNALS

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Acoustic Scene Classification

This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship (EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council

INK-SVD: LEARNING INCOHERENT DICTIONARIES FOR SPARSE REPRESENTATIONS

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning

In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data. We derive s-ipr as a supervised extension of our previously proposed iterative projections and rotations (ipr) algorithm for incoherent dictionary learning, and we employ it to learn incoherent sub-spaces that model signals belonging to different classes. We test our method as a feature transform for supervised classification, first by visualising transformed features from a synthetic dataset and from the ‘iris’ dataset, then by using the resulting features in a classification experiment

Roughness effect on the efficiency of dimer antenna based biosensor

The fabrication process of nanodevices is continually improved. However, most of the nanodevices, such as biosensors present rough surfaces with mean roughness of some nanometers even if the deposition rate of material is more controlled. The effect of roughness on performance of biosensors was fully addressed for plane biosensors and gratings, but rarely addressed for biosensors based on Local Plasmon Resonance. The purpose of this paper is to evaluate numerically the influence of nanometric roughness on the efficiency of a dimer nano-biosensor (two levels of roughness are considered). Therefore, we propose a general numerical method, that can be applied to any other nanometric shape, to take into account the roughness in a three dimensional model. The study focuses on both the far-field, which corresponds to the experimental detected data, and the near-field, responsible for exciting and then detecting biological molecules. The results suggest that the biosensor efficiency is highly sensitive to the surface roughness. The roughness can produce important shifts of the extinction efficiency peak and a decrease of its amplitude resulting from changes in the distribution of near-field and absorbed electric field intensities

Learning incoherent subspaces for classification via supervised iterative projections and rotations

In this paper we present the supervised iterative projections and rotations (S-IPR) algorithm, a method to optimise a set of discriminative subspaces for supervised classification. We show how the proposed technique is based on our previous unsupervised iterative projections and rotations (IPR) algorithm for incoherent dictionary learning, and how projecting the features onto the learned sub-spaces can be employed as a feature transform algorithm in the context of classification. Numerical experiments on the FISHERIRIS and on the USPS datasets, and a comparison with the PCA and LDA methods for feature transform demonstrates the value of the proposed technique and its potential as a tool for machine learning. © 2013 IEEE

Dictionary learning of convolved signals

Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation of them. Starting from this motivation, we develop a block coordinate descent method which aims to learn a convolved dictionary and provide a sparse representation of the observed signals with small residual norm. We compare the proposed approach to the K-SVD dictionary learning algorithm and show through numerical experiment on synthetic signals that, provided some conditions on the problem data, our technique converges in a fixed number of iterations to a sparse representation with smaller residual norm

INK-SVD: Learning incoherent dictionaries for sparse representations

This work considers the problem of learning an incoherent dictionary that is both adapted to a set of training data and incoherent so that existing sparse approximation algorithms can recover the sparsest representation. A new decorrelation method is presented that computes a fixed coherence dictionary close to a given dictionary. That step iterates pairwise decorrelations of atoms in the dictionary. Dictionary learning is then performed by adding this decorrelation method as an intermediate step in the K-SVD learning algorithm. The proposed algorithm INK-SVD is tested on musical data and compared to another existing decorrelation method. INK-SVD can compute a dictionary that approximates the training data as well as K-SVD while decreasing the coherence from 0.6 to 0.2. © 2012 IEEE

On the disjointess of sources in music using different time-frequency representations

This paper studies the disjointness of the time-frequency representations of simultaneously playing musical instruments. As a measure of disjointness, we use the approximate W-disjoint orthogonality as proposed by Yilmaz and Rickard [1], which (loosely speaking) measures the degree of overlap of different sources in the time-frequency domain. The motivation for this study is to find a maximally disjoint representation in order to facilitate the separation and recognition of musical instruments in mixture signals. The transforms investigated in this paper include the short-time Fourier transform (STFT), constant-Q transform, modified discrete cosine transform (MDCT), and pitch-synchronous lapped orthogonal transforms. Simulation results are reported for a database of polyphonic music where the multitrack data (instrument signals before mixing) were available. Absolute performance varies depending on the instrument source in question, but on the average MDCT with 93 ms frame size performed best. © 2011 IEEE

Effects of caspofungin against Candida guilliermondii and Candida parapsilosis.

The in vitro activity of caspofungin (CAS) was investigated against 28 yeast isolates belonging to Candida albicans (n = 5), Candida guilliermondii (n = 10), and Candida parapsilosis (n = 13). CAS MICs obtained by broth dilution and Etest methods clearly showed a rank order of susceptibility to the echinocandin compound with C. albicans > C. parapsilosis > C. guilliermondii. Similarly, time-kill assays performed on selected isolates showed that CAS was fungistatic against C. albicans and C. parapsilosis, while it did not exert any activity against C. guilliermondii. In a murine model of systemic candidiasis, CAS given at doses as low as 1 mg/kg of body weight/day was effective at reducing the kidney burden of mice infected with either C. albicans or C. guilliermondii isolates. Depending on the isolate tested, mice infected with C. parapsilosis responded to CAS given at 1 and/or 5 mg/kg/day. However, the overall CFU reduction for C. guilliermondii and C. parapsilosis was approximately 100-fold less than that for C. albicans. Our study shows that CAS was active in experimental systemic candidiasis due to C. guilliermondii and C. parapsilosis, but this activity required relatively high drug dosages