Korea University Research and Business Foundation

Samsung Electronics Co., Ltd.

Hanwha Techwin Co., Ltd.

Korea University Industrial Academic Collaboration Foundation

Samsung Techwin Co., Ltd.

Hanseok Ko

Hyun-Soo Kim

Jounghoon Beh

Jae-Won Lee

Sung-Soo Kim

Hyun-jin Yoon

Taeyup Song

Sung-Joo Ahn

Taekjin Lee

Younghyun Lee

Hanjun Kim

Dong-Jun Kim

Kyungsun Lee

Bong-hyup Kang

Chang-won Jeon

Kwangyoun Kim

Suwon Shon

Jinsang Rho

Donghyeon Kim

Kyung-Deuk Ko

Sangwook Park

Dubok Park

Bon Hwa Ku

Bonhwa Ku

An earthquake event classification method using an attention-based neural network includes: preprocessing input earthquake data by centering; extracting a feature map by nonlinearly converting the preprocessed earthquake data through a plurality of convolution layers having three or more layers; measuring importance of a learned feature of the nonlinear-converted earthquake data based on an attention technique in which interdependence of channels of the feature map is modeled; correcting a feature value of the measured importance value through element-wise multiply with the learned feature map; performing down-sampling through max-pooling based on the feature value; and classifying an earthquake event by regularizing the down-sampled feature value. Accordingly, main core features inherent in many/complex data are extracted through attention-based deep learning to overcome the limitations of the existing micro earthquake detection technology, thereby enabling earthquake detection even in low SNR environments.

Earthquake event classification method using attention-based convolutional neural network, recording medium and device for performing the method

Disclosed are a method and an apparatus for identifying animal species by using audiovisual information. A method for identifying animal species, according to one embodiment of the present invention, may include: a step of receiving an input signal for an object to be identified; a step of processing image information and acoustic information based on the input signal, wherein a processing result of the image information and a processing result of the acoustic information are represented by class-specific scores; a step of determining whether the image information processing result and the acoustic information processing result corresponding to the input signal exist; and a final result derivation step of fusing the image information processing result and the acoustic information processing result according to the determination result and classifying the object to be identified as a certain animal species by using the fused processing result.

Method and apparatus for identifying animal species

Provided is a method of detecting a voice section, including detecting from at least one image an area where lips exist, obtaining a feature value of movement of the lips in the detected area based on a difference between pixel values of pixels included in the detected area, and detecting the voice section from the at least one image based on the feature value.

Method and device for detecting voice activity based on image information

A method of converting a feature vector includes extracting a feature sequence from an audio signal including utterance of a user; extracting a feature vector from the feature sequence; acquiring a conversion matrix for reducing a dimension of the feature vector, based on a probability value acquired based on different covariance values; and converting the feature vector by using the conversion matrix.

Method and device for transforming feature vector for user recognition

The present invention provides a method for removing a haze in a single image. In the present invention, a transmission is estimated by using a dark channel prior obtained from a hazy input image. The estimated transmission includes a block artifact. In an exemplary embodiment of the present invention, in order to preserve an edge and remove the block artifact, a refined transmission value is obtained by performing WLS filtering by using an estimated transmission value and a morphologically-processed input image, the image is restored based on the refined transmission value, and then multi-scale tone manipulation image processing is performed.

Method and device for removing haze in single image

A surveillance system and method are provided. The surveillance system includes an audio processing device which extracts an audio feature of an audio signal, and determines whether an abnormal event has occurred in a monitoring region, based on the audio feature; a video processing device which extracts a foreground region from a video signal, and determines whether an abnormal event has occurred in the monitoring region, based on motion information of the foreground region; and a context awareness device which calculates an audio abnormal probability and a video abnormal probability by respectively accumulating results of abnormal event occurrence/non-occurrence determinations performed on audio signals and results of abnormal event occurrence/non-occurrence determinations performed on video signals for a certain period of time, and finally determines whether an abnormal situation has occurred in the monitoring region, by using respective combined probability distribution models for a normal situation and the abnormal situation.

Indoor surveillance system and indoor surveillance method

An adaptive mode control apparatus and method for adaptive beamforming based on detection of a user direction sound are provided. The adaptive mode control apparatus includes a signal intensity detector that searches for signal intensity of each designated direction to detect signal intensity having a maximum value when a voice signal of each direction is input through at least one microphone; and an adaptive mode controller that compares the signal intensity having the maximum value detected through the signal intensity detector with a threshold value and determines whether to perform an adaptive mode of a Generalized Sidelobe Canceller (GSC) according to the comparison results. Therefore, a lack of control of adaptation of an adaptive filter of the conventional art is solved. That is, as one condition for guaranteeing performance of adaptive beamforming, adaptation of an adaptive filter is not performed when noise of a sound with a high autocorrelation is cancelled.

Adaptive mode control apparatus and method for adaptive beamforming based on detection of user direction sound

A system and method for sound source separation. The system and method use a beamforming technique. The sound source separation system includes a windowing processor; a DFT transformer; a transfer function estimator; and a noise estimator. The system also includes a voice signal extractor that cancels individual voice signals, except an individual voice signal that is desired to be extracted among individual voice signals, from the integrated voice signals. The system further includes a voice signal detector that cancels a noise part provided through the noise estimator from a transfer function of an individual voice signal which is desired to be detected and extracts a noise-canceled individual voice signal. Even when two or more sound sources are simultaneously input, the sound sources can be separated from each other and separately stored and managed, or an initial sound source can be stored and managed.

Sound source separation method and system using beamforming technique

Provided are a method and apparatus for processing an image. The method includes receiving a first luminance image of an image including airlight, which is a type of light that occurs in a foggy environment, and generating an airtight map based on a ratio between an average luminance of the first luminance image, and a standard deviation; and removing the airtight by subtracting the airtight map from the first luminance image and outputting a second luminance image. According to the present invention, airlight components may be effectively removed.

Image processing method and apparatus for correcting distortion caused by air particles as in fog

Disclosed is a method and an apparatus for estimating noise included in a sound signal during sound signal processing. The method includes estimating harmonics components in a frame of an input sound signal; using the estimated harmonics components, computing a Voice Presence Probability (VPP) on the frame of the input sound signal; determining a weight of an equation necessary to estimate a noise spectrum, depending on the computed VPP; and using the determined weight and the equation necessary to estimate a noise spectrum, estimating the noise spectrum, and updating the noise spectrum.

Method and apparatus for estimating noise by using harmonics of voice signal

Disclosed are an apparatus and a method for beamforming in consideration of characteristics of an actual noise environment. The apparatus includes a microphone array having at least microphone, the microphone array outputting a signal input through the microphone; a coherence function generation unit for calculating coherences for input signals according to each space between microphones, calculating averages of the coherences for the same distance, and filtering the calculated averages of the coherences and outputting the resultant values, when an input signal is input; a spatial filter factor calculation unit for calculating and outputting a spatial filter factor by using the filtered average coherences; and a beamforming execution unit for performing a beamforming for the input signals by using the spatial filter factor, thereby outputting a noise-processed signal.

Growing community of inventors

Seoul, South Korea

Hanseok Ko