Abstract: A method is provided for detecting the presence of an anomaly within an observed physical signal. The observed signal includes an addition of a physical disturbance signal and a reference signal. The anomaly is relative to a change in the behavior of the reference signal compared with a first tolerance value. Such a method includes: determining a time span having at least one moment of interest; detecting the presence of the anomaly within the observed physical signal during the time span by conducting a hypothesis test using the first tolerance value, a first rate of tolerated false alarms, and data obtained from processing the observed signal.

Abstract: A method is provided for detecting the presence of an anomaly within an observed physical signal. The observed signal includes an addition of a physical disturbance signal and a reference signal. The anomaly is relative to a change in the behavior of the reference signal compared with a first tolerance value. Such a method includes: determining a time span having at least one moment of interest; detecting the presence of the anomaly within the observed physical signal during the time span by conducting a hypothesis test using the first tolerance value, a first rate of tolerated false alarms, and data obtained from processing the observed signal.

Abstract: The disclosed method uses the Wiener frequency filtering to suppress noise in noisy sound signals (u(t)). This method includes a preliminary step in which the sound signals (u(t)) to be noise-suppressed are digitized by sampling and subdivided into frames. The method then includes a first series of steps including the creation of a noise model on N frames, the estimating of the spectral density of the noise and of the energy of the noise model and the computing of a coefficient that reflects the statistical dispersion of the noise. It also includes a second series of steps including the computation of the spectral density of the signals to be noise-suppressed fore each frame. The coefficients of the Wiener filter are modified for each successively processed frame, by the parameters determined at the end of the two series of steps, so as to introduce an energy compensation and an adaptive overestimation of the noise.

Abstract: A process for the denoising of audio signals picked up in a noisy environment, for example in the cockpit of an aircraft or of another vehicle, and more precisely to the searching for a noise model in the audio signals. Input signals are digitized, and these signals are processed on the basis of a noise model, in principle with a view to eliminate as far as possible the noise corresponding to the model.

Abstract: A process for voice recognition in a harsh environment such as the cockpit of a military aircraft. An acquisition device is connected to an acoustico-phonetic decoding device which is then connected to a recognition supervisor. The acquisition device is also connected to a voice recognition device which is also connected to the recognition supervisor. The voice recognition device is also connected to a dictionary and to a syntax describing device. The syntax describing device is connected to a dialogue model as well as the recognition supervisor.

Abstract: A method for detecting the start and end of speech from a noisy signal including the steps of:detecting a voiced frame;searching for noise frames preceding this voiced frame;constructing an autoregressive model of the noise and a mean noise spectrum;bleaching the flames preceding the voicing,searching for the actual start of speech in the bleached frames;removing the noise from the voiced frames and parameterizing them; andsearching for the actual end of speech.

Abstract: The energy-based process according to the invention for the detection of useful signals drowned in noise consists of starting from a frame of samples of a noisy signal grouped in successive frames, making a pre-classification by comparing the energies of successive samples of each frame with a determined optimum threshold and sorting samples which have a high probability of belonging to a "noise only" class into this class, and then for each of these samples detecting those that have a sufficiently high energy so that they have a high probability of belonging to a "noise+useful signal" class, this second class being defined using the first class as a reference.

Abstract: In order to detect a useful signal affected by noise, a measurement is taken of the expected S/N ratio of this signal over a time slice, a measurement of the estimated white noise alone is taken over another time slice without useful signal, the mean energy of the noise and of the noise-affected signal is calculated, in each of their time slices, the theoretical detection threshold is calculated, the ratio of these two energies is calculated, and the ratio is compared with the calculated threshold, this threshold being greater than 1 (ideal threshold).