An aircraft noise monitoring system uses a set of geographically distributed noise sensors to receive data corresponding to events captured by the noise sensors. Each event corresponds to noise that exceeds a threshold level. For each event, the system will receive a classification of the event as an aircraft noise event or a non-aircraft noise event. It will then use the data corresponding to the events and the received classifications to train a convolutional neural network (CNN) in a classification process. After training, when the system receives a new noise event, it will use the CNN to classify the new noise event as an aircraft noise event or a non-aircraft noise event, and it will generate an output indicating whether the new noise event is an aircraft noise event or a non-aircraft noise event.

A device for outputting sound and a method therefor are provided. The sound output method includes predicting external sound to be received from an external environment, variably adjusting sound to be output from the device, based on the predicted external sound, and outputting the adjusted sound.

A device for outputting sound and a method therefor are provided. The sound output method includes predicting external sound to be received from an external environment, variably adjusting sound to be output from the device, based on the predicted external sound, and outputting the adjusted sound.

According to a first aspect of the present disclosure, a method for facilitating the detection of one or more time series patterns is conceived, comprising building one or more artificial neural networks, wherein, for at least one time series pattern to be detected, a specific one of said artificial neural networks is built. According to a second aspect of the present disclosure, a corresponding computer program is provided. According to a third aspect of the present disclosure, a non-transitory computer-readable medium is provided that comprises a computer program of the kind set forth. According to a fourth aspect of the present disclosure, a corresponding system for facilitating the detection of one or more time series patterns is provided.

An information-processing device includes a first feature-value information acquiring unit for acquiring an acoustic feature-value vector and a language feature-value vector extracted from a user's spoken voice. The information-processing device includes a second feature-value information acquiring unit for acquiring an image feature-value vector extracted from the user's facial image. The information-processing device includes an emotion estimating unit including a learned model including: a first attention layer using, as inputs, a first vector generated from the acoustic feature-value vector and a second vector generated from the image feature-value vector; and a second attention layer using, as an input, an output vector from the first attention layer and a third vector generated from the language feature-value vector, wherein the emotion estimating unit is for estimating the user's emotion based on the output vector from the second attention layer.

Provided is an automated interpretation method, apparatus, and system. The automated interpretation method includes encoding a voice signal in a first language to generate a first feature vector, decoding the first feature vector to generate a first language sentence in the first language, encoding the first language sentence to generate a second feature vector with respect to a second language, decoding the second feature vector to generate a second language sentence in the second language, controlling a generating of a candidate sentence list based on any one or any combination of the first feature vector, the first language sentence, the second feature vector, and the second language sentence, and selecting, from the candidate sentence list, a final second language sentence as a translation of the voice signal.

Provided is an automated interpretation method, apparatus, and system. The automated interpretation method includes encoding a voice signal in a first language to generate a first feature vector, decoding the first feature vector to generate a first language sentence in the first language, encoding the first language sentence to generate a second feature vector with respect to a second language, decoding the second feature vector to generate a second language sentence in the second language, controlling a generating of a candidate sentence list based on any one or any combination of the first feature vector, the first language sentence, the second feature vector, and the second language sentence, and selecting, from the candidate sentence list, a final second language sentence as a translation of the voice signal.

Techniques are provided for audio-based detection and tracking of an acoustic source. A methodology implementing the techniques according to an embodiment includes generating acoustic signal spectra from signals provided by a microphone array, and performing beamforming on the acoustic signal spectra to generate beam signal spectra, using time-frequency masks to reduce noise. The method also includes detecting, by a deep neural network (DNN) classifier, an acoustic event, associated with the acoustic source, in the beam signal spectra. The DNN is trained on acoustic features associated with the acoustic event. The method further includes performing pattern extraction, in response to the detection, to identify time-frequency bins of the acoustic signal spectra that are associated with the acoustic event, and estimating a motion direction of the source relative to the array of microphones based on Doppler frequency shift of the acoustic event calculated from the time-frequency bins of the extracted pattern.

Techniques are provided for audio-based detection and tracking of an acoustic source. A methodology implementing the techniques according to an embodiment includes generating acoustic signal spectra from signals provided by a microphone array, and performing beamforming on the acoustic signal spectra to generate beam signal spectra, using time-frequency masks to reduce noise. The method also includes detecting, by a deep neural network (DNN) classifier, an acoustic event, associated with the acoustic source, in the beam signal spectra. The DNN is trained on acoustic features associated with the acoustic event. The method further includes performing pattern extraction, in response to the detection, to identify time-frequency bins of the acoustic signal spectra that are associated with the acoustic event, and estimating a motion direction of the source relative to the array of microphones based on Doppler frequency shift of the acoustic event calculated from the time-frequency bins of the extracted pattern.

Volume leveler controller and controlling method are disclosed. In one embodiment, A volume leveler controller includes an audio content classifier for identifying the content type of an audio signal in real time; and an adjusting unit for adjusting a volume leveler in a continuous manner based on the content type as identified. The adjusting unit may configured to positively correlate the dynamic gain of the volume leveler with informative content types of the audio signal, and negatively correlate the dynamic gain of the volume leveler with interfering content types of the audio signal.