Abstract: This method comprises steps of: a) partitioning (10, 16) the spectrum of the noisy signal into a HF part and a LF part; b) operating denoising processes in a differentiated manner for each of the two parts of the spectrum with, for the HF part, a denoising by prediction of the useful signal from one sensor to the other between sensors of a first sub-array (R1), by means of a first adaptive algorithm estimator (14), and, for the LF part, a denoising by prediction of the noise from one sensor to the other between sensors of a second sub-array (R2), by means of a second adaptive algorithm estimator (18); c) reconstructing the spectrum by combining together (22) the signals delivered after denoising of the two parts of the spectrum, respectively; and d) selectively reducing the noise (24) by an Optimized Modified Log-Spectral Amplitude gain, OM-LSA, process.

Abstract: The method comprises the steps of: digitizing sound signals picked up simultaneously by two microphones (N, M); executing a short-term Fourier transform on the signals (xn(t), xm(t)) picked up on the two channels so as to produce a succession of frames in a series of frequency bands; applying an algorithm for calculating a speech-presence confidence index on each channel, in particular a probability a speech that is present; selecting one of the two microphones by applying a decision rule to the successive frames of each of the channels, which rule is a function both of a channel selection criterion and of a speech-presence confidence index; and implementing speech processing on the sound signal picked up by the one microphone that is selected.

Abstract: The headset comprises: a physiological sensor suitable for being coupled to the cheek or the temple of the wearer of the headset and for picking up non-acoustic voice vibration transmitted by internal bone conduction; lowpass filter means for filtering the signal as picked up; a set of microphones picking up acoustic voice vibration transmitted by air from the mouth of the wearer of the headset; highpass filter means and noise-reduction means for acting on the signals picked up by the microphones; and mixer means for combining the filtered signals to output a signal representative of the speech uttered by the wearer of the headset. The signal of the physiological sensor is also used by means for calculating the cutoff frequency of the lowpass and highpass filters and by means for calculating the probability that speech is absent.

Abstract: The equipment comprises two microphones, sampling means, and de-noising means. The de-noising means are non-frequency noise reduction means comprising a combiner having an adaptive filter performing an iterative search seeking to cancel the noise picked up by one of the microphones on the basis of a noise reference given by the other microphone sensor. The adaptive filter is a fractional delay filter modeling a delay that is shorter than the sampling period. The equipment also has voice activity detector means delivering a signal representative of the presence or the absence of speech from the user of the equipment. The adaptive filter receives this signal as input so as to enable it to act selectively: i) either to perform an adaptive search for the parameters of the filter in the absence of speech; ii) or else to “freeze” those parameters of the filter in the presence of speech.

Abstract: This method comprises steps of: a) partitioning (10, 16) the spectrum of the noisy signal into a HF part and a LF part; b) operating denoising processes in a differentiated manner for each of the two parts of the spectrum with, for the HF part, a denoising by prediction of the useful signal from one sensor to the other between sensors of a first sub-array (R1), by means of a first adaptive algorithm estimator (14), and, for the LF part, a denoising by prediction of the noise from one sensor to the other between sensors of a second sub-array (R2), by means of a second adaptive algorithm estimator (18); c) reconstructing the spectrum by combining together (22) the signals delivered after denoising of the two parts of the spectrum, respectively; and d) selectively reducing the noise (24) by an Optimized Modified Log-Spectral Amplitude gain, OM-LSA, process.

Abstract: A multi-microphone hands-free device operating in noisy surroundings implements a method of de-noising a noisy sound signal. The noisy sound signal comprises a useful speech component coming from a directional speech source and an unwanted noise component, the noise component itself including a lateral noise component that is non-steady and directional. The method operates in the frequency domain and comprises combining signals into a noisy combined signal, estimating a pseudo-steady noise component, calculating a probability of transients being present in the noisy combined signal, estimating a main arrival direction of transients, calculating a probability of speech being present on the basis of a three-dimensional spatial criterion suitable for discriminating amongst the transients between useful speech and lateral noise, and selectively reducing noise by applying a variable gain specific to each frequency band and to each time frame.

Abstract: The equipment comprises two microphones, sampling means, and de-noising means. The de-noising means are non-frequency noise reduction means comprising a combiner having an adaptive filter performing an iterative search seeking to cancel the noise picked up by one of the microphones on the basis of a noise reference given by the other microphone sensor. The adaptive filter is a fractional delay filter modeling a delay that is shorter than the sampling period. The equipment also has voice activity detector means delivering a signal representative of the presence or the absence of speech from the user of the equipment. The adaptive filter receives this signal as input so as to enable it to act selectively: i) either to perform an adaptive search for the parameters of the filter in the absence of speech; ii) or else to “freeze” those parameters of the filter in the presence of speech.

Abstract: The method comprises the steps of: digitizing sound signals picked up simultaneously by two microphones (N, M); executing a short-term Fourier transform on the signals (xn(t), xm(t)) picked up on the two channels so as to produce a succession of frames in a series of frequency bands; applying an algorithm for calculating a speech-presence confidence index on each channel, in particular a probability a speech that is present; selecting one of the two microphones by applying a decision rule to the successive frames of each of the channels, which rule is a function both of a channel selection criterion and of a speech-presence confidence index; and implementing speech processing on the sound signal picked up by the one microphone that is selected.

Abstract: The headset comprises: a physiological sensor suitable for being coupled to the cheek or the temple of the wearer of the headset and for picking up non-acoustic voice vibration transmitted by internal bone conduction; lowpass filter means for filtering the signal as picked up; a set of microphones picking up acoustic voice vibration transmitted by air from the mouth of the wearer of the headset; highpass filter means and noise-reduction means for acting on the signals picked up by the microphones; and mixer means for combining the filtered signals to output a signal representative of the speech uttered by the wearer of the headset. The signal of the physiological sensor is also used by means for calculating the cutoff frequency of the lowpass and highpass filters and by means for calculating the probability that speech is absent.

Abstract: A multi-microphone hands-free device distinguishes between non-steady noise and speech and adapts the de-noising to the presence and characteristics of the detected non-steady noise without spoiling any speech that is present. In the frequency domain, the method comprises calculating a first noise reference by analyzing spatial coherence of signals picked up, calculating a second noise reference by analyzing directions of incidence of signals picked up, estimating a main direction of incidence of signals picked up, selecting as a referent noise signal noise references as a function of estimated main direction, combining signals picked up into a noisy combined signal, calculating probability that speech is absent in the noisy combined signal on basis of respective spectral energy levels of the noisy combined signal and of the referent noise signal, and selectively reducing noise by applying variable gain that is specific to each frequency band and to each time frame.

Abstract: In the frequency domain, the method comprises the following steps: a) calculating a first noise reference by analyzing the spatial coherence of the signals picked up; b) calculating a second noise reference by analyzing the directions of incidence of the signals picked up; c) estimating a main direction of incidence of the signals picked up; d) selecting as a referent noise signal one or the other of the noise references as a function of the estimated main direction; e) combining the signals picked up into a noisy combined signal; f) calculating a probability that speech is absent in the noisy combined signal on the basis of the respective spectral energy levels of the noisy combined signal and of the referent noise signal; and g) selectively reducing the noise by applying variable gain that is specific to each frequency band and to each time frame.

Abstract: The method comprises the following steps in the frequency domain: a) combining signals into a noisy combined signal; b) estimating a pseudo-steady noise component; c) calculating a probability of transients being present in the noisy combined signal; d) estimating a main arrival direction of transients; e) calculating a probability of speech being present on the basis of a three-dimensional spatial criterion suitable for discriminating amongst the transients between useful speech and lateral noise; and f) selectively reducing noise by applying a variable gain specific to each frequency band and to each time frame.