This paper deals with the eddy current technique for nondestructive evaluation of the crack depth on a massive specimen used in aeronautical industry. A set of C-scan eddy current images is analyzed to reduce the noise and to select suitable features, which can be used to estimate the crack depth. Based on this study, a method relying on polynomial forward models of the relationship between crack depth and the maximum value of the sensor impedance is proposed. The least square and the non-negative least square techniques are applied to analyze the usability of proposed models. The error of obtained estimations is smaller than 10%, for almost used experimental data.

As blind audio source separation has remained very challenging in real-world scenarios, some existing works, including ours, have investigated the use of a weakly-informed approach where generic source spectral models (GSSM) can be learned a priori based on nonnegative matrix factorization (NMF). Such approach was derived for single-channel audio mixtures and shown to be efficient in different settings. This paper proposes a multichannel source separation approach where the GSSM is combined with the source spatial covariance model within a unified Gaussian modeling framework. We present the generalized expectation-minimization (EM) algorithm for the parameter estimation. Especially, for guiding the estimation of the intermediate source variances in each EM iteration, we investigate the use of two criteria: (1) the estimated variances of each source are constrained by NMF, and (2) the total variances of all sources are constrained by NMF altogether. While the former can be seen as a source variance denoising step, the latter is viewed as an additional separation step applied to the source variance. We demonstrate the speech separation performance, together with its convergence and stability with respect to parameter setting, of the proposed approach using a benchmark dataset provided within the 2016 Signal Separation Evaluation Campaign. KEYWORDSMultichannel audio source separation, local Gaussian model, nonnegative matrix factorization, generic spectral model, group sparsity constraint.

This paper focuses on solving a challenging speech enhancement problem: improving the desired speech from a singlechannel audio signal containing high-level unspecified noise (possibly environmental noise, music, other sounds, etc.). Using source separation technique, we investigate a solution combining nonnegative matrix factorization (NMF) with mixed group sparsity constraint that allows exploiting generic noise spectral model to guide the separation process. The experiment performed on a set of benchmarked audio signals with different types of real-world noise shows that the proposed algorithm yields better quantitative results in term of the signal-to-distortion ratio than the previously published algorithms.