Emotional speaker recognition in real life conditions using multiple descriptors and i-vector speaker modeling technique

Multimedia Tools and Applications - Emotional speaker recognition under real life conditions becomes an urgent need for several applications. This paper proposes a novel approach using multiple...     

Speech enhancement based on perceptual filter bank improvement

In this paper, a new denoising approach is presented based on perceptual analysis. The noisy signal is split by a gammatone filterbank with nonlinear frequency distributions according to ERB scale. The frequency masking threshold is calculated in each sub-band according to the Johnston model in the output of the Wiener filter. This threshold is then used in the gain function given by the perceptual filter. The evaluation tests are performed by using objective criterion including perceptual evaluation of speech quality as well as subjective criterion including mean opinion score. Obtained results show that the proposed method achieves best results in terms of quality and intelligibility of enhanced signal.     

Underdetermined blind source separation technique based on speech features extraction

This paper presents a new time–frequency approach for recovering source contribution to two convolutive mixtures. The separation task is performed on two steps: Each mixture is clustered into voiced/unvoiced frames, and then the predominant source in each time frequency bin is identified through a specific weight function which is based on sources’ excitation characteristics extraction. We investigate the performance of the proposed approach in the underdetermined context using objective quality measures. Results for separating three and four speech sources in a live recorded mixture show the superiority of the proposed method in rejecting artifacts over existing convolutive separation techniques.     

A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C.elegans

Multimedia Tools and Applications - The Helitrons, an important sub-class of the transposable elements (TEs) class II, have been revealed in diverse eukaryotic genomes. They are mobile elements...     

Multiclass support vector machines for environmental sounds classification in visual domain based on log-Gabor filters

This paper presents an approach aimed at recognizing environmental sounds for surveillance and security applications. We propose a robust environmental sound classification approach, based on spectrograms features derive from log-Gabor filters. This approach includes three methods. In the first two methods, the spectrograms are passed through an appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criteria. The third method uses the same steps but applied only to three patches extracted from each spectrogram. To investigate the accuracy of the proposed methods, we conduct experiments using a large database containing 10 environmental sound classes. The classification results based on Multiclass Support Vector Machines show that the second method is the most efficient with an average classification accuracy of 89.62 %.     

Covariance-guided landmine detection and discrimination using ground-penetrating radar data

Ground penetrating Radar (GPR) can detect and deliver the response signal from any buried kind of object like plastic or metallic landmines, stones, and wood sticks. It delivers three kinds of data: Ascan, Bscan, and Cscan. However, it cannot discriminate between landmines and inoffensive objects or ‘clutter.’ One-class classification is an alternative to detect landmines, especially, as landmines features data are unbalanced. In this article, we investigate the effectiveness of the Covariance-guided One-Class Support Vector Machine (COSVM) to detect, discriminate, and locate landmines efficiently. In fact, compared to existing one-class classifiers, the COSVM has the advantage of emphasizing low variance directions. Moreover, we will compare the one-class classification to multiclass classification to tease out the advantage of the former over the latter as data are unbalanced. Our method consists of extracting Ascan GPR data. Extracted features are used as an input for COSVM to discriminate between landmines and clutter. We provide an extensive evaluation of our detection method compared to other methods based on relevant state of the art one-class and multiclass classifiers, on the well-known MACADAM database. Our experimental results show clearly the superiority of using COSVM in landmine detection and localization.