Cedric Gervaise

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In last two decades, many researchers have been involved in acoustic tomography applications. Recently, few algorithms have been dedicated to the passive acoustic tomography applications in a single input single output channel. Unfortunately, most of these algorithms can not be applied in a real situation when we have a Multi-Input Multi-Output channel. In(More)
Time–frequency representations constitute the main tool for analysis of non–stationary signals arising from environmental systems. Recently , the interest for underwater dispersive channels appears since dispersivity phenomena act at very low frequencies which are well suited for long range underwater communication. In such a case, a main interest is to(More)
Blind deconvolution is presented in the underwater acoustic channel context, by time-frequency processing. The acoustic propagation environment was modelled as a multipath propagation channel. For noiseless simulated data, source signature estimation was performed by a model-based method. The channel estimate was obtained via a time-frequency formulation of(More)
The estimation of the impulse response (IR) of a propagation channel is necessary for a large number of acoustic applications: underwater communication, detection and localization, etc. Basically, it informs us about the distortions of a transmitted signal in one channel. This operation is usually subject to additional distortions due to the motion of the(More)
— This paper presents an automatic and passive local-ization algorithm for low frequency impulsive sources in shallow water. This algorithm is based on the normal mode theory which characterizes propagation in this configuration. It uses specific signal processing tools and time-frequency representations to automatically extract features of the propagation.(More)
Many marine mammals produce highly nonlinear frequency modulations. Determining the time-frequency support of these sounds offers various applications, which include recognition, localization, and density estimation. This study introduces a low parameterized automated spectrogram segmentation method that is based on a theoretical probabilistic framework. In(More)
The analysis of signals consisting of multiple components with non-linear frequency modulation is required in a large number of applications, including the study of marine mammals vocalizations. This analysis has multiple motivations, such as investigating the impact of anthropogenic noise on marine mammal behavior, and species identification to avoid(More)