A bioinspired spectro-temporal domain for sound denoising

The representation of sound signals at the cochlea and au- ditory cortical level has been studied as an alternative to classical anal- ysis methods. In this work, we put forward a recently proposed feature extraction method called approximate auditory cortical representation, based on an approximation to the statistics of discharge patterns at the primary auditory cortex. The approach here proposed estimates a non- negative sparse coding with a combined dictionary of atoms calculated from clean signal and noise. The denoising is carried out on noisy signals by the reconstruction of the signal discarding the atoms corresponding to the noise. Results on synthetic and real data show that the proposed method improves the quality of the signals, mainly under severe degra- dation. This communication corresponds to a journal paper published in 2015 in DSP (Elsevier).Sociedad Argentina de Informática e Investigación Operativa (SADIO

A bioinspired spectro-temporal domain for sound denoising

The representation of sound signals at the cochlea and au- ditory cortical level has been studied as an alternative to classical anal- ysis methods. In this work, we put forward a recently proposed feature extraction method called approximate auditory cortical representation, based on an approximation to the statistics of discharge patterns at the primary auditory cortex. The approach here proposed estimates a non- negative sparse coding with a combined dictionary of atoms calculated from clean signal and noise. The denoising is carried out on noisy signals by the reconstruction of the signal discarding the atoms corresponding to the noise. Results on synthetic and real data show that the proposed method improves the quality of the signals, mainly under severe degra- dation. This communication corresponds to a journal paper published in 2015 in DSP (Elsevier).Sociedad Argentina de Informática e Investigación Operativa (SADIO

An integrative investigation of sensory organ development and orientation behavior throughout the larval phase of a coral reef fish

The dispersal of marine larvae determines the level of connectivity among populations, influences population dynamics, and affects evolutionary processes. Patterns of dispersal are influenced by both ocean currents and larval behavior, yet the role of behavior remains poorly understood. Here we report the first integrated study of the ontogeny of multiple sensory systems and orientation behavior throughout the larval phase of a coral reef fish—the neon goby, Elacatinus lori. We document the developmental morphology of all major sensory organs (lateral line, visual, auditory, olfactory, gustatory) together with the development of larval swimming and orientation behaviors observed in a circular arena set adrift at sea. We show that all sensory organs are present at hatch and increase in size (or number) and complexity throughout the larval phase. Further, we demonstrate that most larvae can orient as early as 2 days post-hatch, and they swim faster and straighter as they develop. We conclude that sensory organs and swimming abilities are sufficiently developed to allow E. lori larvae to orient soon after hatch, suggesting that early orientation behavior may be common among coral reef fishes. Finally, we provide a framework for testing alternative hypotheses for the orientation strategies used by fish larvae, laying a foundation for a deeper understanding of the role of behavior in shaping dispersal patterns in the sea

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments

Correlated Postfiltering and Mutual Information in Pseudoanechoic Model Based Blind Source Separation

In a recent publication the pseudoanechoic mixing model for closely spaced microphones was proposed and a blind audio sources separation algorithm based on this model was developed. This method uses frequency-domain independent component analysis to identify the mixing parameters. These parameters are used to synthesize the separation matrices, and then a time-frequency Wiener postfilter to improve the separation is applied. In this contribution, key aspects of the separation algorithm are optimized with two novel methods. A deeper analysis of the working principles of the Wiener postfilter is presented, which gives an insight in its reverberation reduction capabilities. Also a variation of this postfilter to improve the performance using the information of previous frames is introduced. The basic method uses a fixed central frequency bin for the estimation of the mixture parameters. In this contribution an automatic selection of the central bin, based in the information of the separability of the sources, is introduced. The improvements obtained through these methods are evaluated in an automatic speech recognition task and with the PESQ objective quality measure. The results show an increased robustness and stability of the proposed method, enhancing the separation quality and improving the speech recognition rate of an automatic speech recognition system.Fil: Di Persia, Leandro Ezequiel. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Milone, Diego Humberto. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Departamento de Informática. Laboratorio de Investigaciones En Señales E Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Yanagida, Masuzo . Doshisha University. Department of Intelligent Information Engineering and Science; Estados Unido

Complexity-Based Discrepancy Measures Applied to Detection of Apnea-Hypopnea Events

In recent years, an increasing interest in the development of discriminative methods based on sparse representations with discrete dictionaries for signal classification has been observed. It is still unclear, however, what is the most appropriate way for introducing discriminative information into the sparse representation problem. It is also unknown which is the best discrepancy measure for classification purposes. In the context of feature selection problems, several complexity-based measures have been proposed. The main objective of this work is to explore a method that uses such measures for constructing discriminative subdictionaries for detecting apnea-hypopnea events using pulse oximetry signals. Besides traditional discrepancy measures, we study a simple one called Difference of Conditional Activation Frequency (DCAF). We additionally explore the combined effect of overcompleteness and redundancy of the dictionary as well as the sparsity level of the representation. Results show that complexity-based measures are capable of adequately pointing out discriminative atoms. Particularly, DCAF yields competitive averaged detection accuracy rates of 72.57% at low computational cost. Additionally, ROC curve analyses show averaged diagnostic sensitivity and specificity of 81.88% and 87.32%, respectively. This shows that discriminative subdictionary construction methods for sparse representations of pulse oximetry signals constitute a valuable tool for apnea-hypopnea screening