Abstract:
A device and method for calculating scattering features for audio signal recognition. An interface receives an audio signal that is processed by at least one processor to obtain an audio frame. The processor calculates a first order scattering features from at least one audio frame and then calculates for the first order scattering features an estimation of whether the first order scattering features comprises sufficient information for accurate audio signal recognition. The processor calculates a second order scattering features from the first order scattering features only in case the first order scattering features does not comprise sufficient information for accurate audio signal recognition. As second order features are calculated only when it is deemed necessary, less processing power can be used by the device, which can lead to less power used by the device.
Abstract:
The invention relates to a storage device management method allowing to manage the storage space, on a storage device, by proposing to an end user to store a new content he was going to consume if its storage determined size is lower than an already stored content size.
Abstract:
A method and device is described and includes: obtaining an input audio signal associated with the voice input, obtaining a transcript resulting from the processing of the input audio signal, converting the transcript into a synthesized audio signal; extracting an acoustic feature of a same type from the input audio signal and synthesized audio signal, delivering a first sequence of features vectors associated with the input audio signal and a second sequence of features vectors associated with the synthesized audio signal converting the acoustic features to corresponding acoustic features associated with a target reference voice, delivering a first sequence and a second sequence of converted features vectors computing a dynamic time warping distance between the first sequence and second sequence of converted features vectors, and delivering data representative of a detection of an audio adversarial attack, as a result of a comparison between the dynamic time warping distance and a predetermined threshold.
Abstract:
A device and method for walker identification. An audio input interface obtains a sampled acoustic signal, possibly from a microphone, a vibration input interface obtains a sampled vibration signal, possibly from a geophone and at least one hardware processor fuses the sampled acoustic signal and the sampled vibration signal into a fused signal, extracts features from the fused signal and identifies a walker based on extracted features.
Abstract:
A device and a method for controlling lighting conditions of a room. The room has at least one adjustable element that impacts the lighting conditions of the room. A multidirectional light sensor including a plurality of light sensors is arranged to capture the light intensity from a plurality of directions. The device obtains measurements from the multidirectional light sensor and adjusts the lighting conditions of the room. When the light in direction of the screen is higher than a first threshold, the device decreases the lighting conditions by sending a command to the adjustable element located in opposite direction to the screen. When the average value of light is lower than a second threshold, the device increases the lighting conditions by sending a command to at least one adjustable element of the room.
Abstract:
The present principles generally relate to audio apparatus, methods, and computer program products and in particular, to improvements that adjust the sound level or levels of one or more audio outputs of an audio system based on the determined origin and/or direction of propagation of a detected human voice in a location. Such an adjustment may be to decrease, mute, or increase the sound level of an audio output producing sound in the direction of the origin of the voice. A sound level produced by other audio outputs may be unchanged.
Abstract:
An electronic apparatus includes a communication unit that communicates with a tag located within a target region when the communication unit is active within the target region, and a processor configured to monitor an activity within the target region based on status information from the tag received by the communication unit, wherein the activity comprises at least one of opening and closing of a member arranged within the target region
Abstract:
A method and device for detecting an audio adversarial attack with respect to a voice command processed by an automatic speech recognition system is described. The method is implemented by a detection device connected to the automatic speech recognition system and includes obtaining an audio signal associated with the voice command, performing a phonetic transcription of the audio signal, according to a phonetic transcription scheme, delivering a first character string; obtaining a transcript resulting from the processing, by the automatic speech recognition system, of the audio signal, performing a phonetic transcription of the transcript, according to the phonetic transcription scheme, delivering a second character string, computing a similarity score between the first character string and the second character string, and delivering a piece of data representative of a detection of an audio adversarial attack, as a function of a result of a comparison between the similarity score and a predetermined threshold.
Abstract:
The disclosure relates to a method for recognizing at least one naturally emitted sound produced by a real-life sound source in an environment comprising at least one artificial sound source. The method is implemented by an audio recognition device, and it includes simultaneously obtaining a first audio signal from a first microphone located in the environment and a second audio signal from an audio acquisition device associated with the at least one artificial sound source; analyzing the first audio signal, delivering a first list of sound classes corresponding to sounds recognized in the first audio signal; analyzing the second audio signal, delivering a second list of sound classes corresponding to sounds recognized in the second audio signal; and delivering a third list of sound classes, comprising only sound classes included in the first list of sound classes which are not included in the second list of sound classes.
Abstract:
A method for detecting anomalies, the method being performed by a machine learning system configured for learning at least one model from a set of training data, the method including receiving sensor data from a plurality of N sensors, computing an anomaly prediction based on the sensor data and the at least one model, and if the anomaly prediction is an anomaly detection, sending an anomaly event containing said anomaly prediction. The method further includes receiving a user feedback relating to said anomaly event or to an absence of anomaly event, and adapting the at least one model based on the user feedback.