Industry-university Cooperation Foundation Hanyang University

Samsung Electronics Co., Ltd.

Hyundai Motor Company

Kia Corporation

Lg Display Co., Ltd.

Kia Motors Corporation

Korea Institute of Science and Technology

Joon-Hyuk Chang

Jin Woo Choi

In-Soo Jung

Youn Ho Kim

Sang Kon Bae

Tae-jun Park

Joon-Young Yang

Jehyun Kyung

Dong-Chul Lee

Joonyoung Yang

Jeong-Hwan Choi

Inmo Yeon

Chang Mok Choi

Ig Jae Kim

Inyoung Hwang

Kang-eun Lee

Hyun-Chul Choi

Kwanghwan Ji

Kiseok Chang

Byeong-seob Ko

Chang Seok Bae

Seung-yeol Lee

Hyoung Gon Kim

Byeong-yong Jeon

Jae-Min Jin

Hyojung Park

Hyeon-seong Kim

Seong-hyeon Choe

Jin Young Son

Kipyo Hong

Junghoon Seo

Tae-hyun Yoon

Kwang-sub Song

Bong-Ki Lee

Dong-Yeoup Jeon

Kwan-Ho Park

Seunghyuck Lee

Kyoungjin Noh

Joo-Hyun Lee

Song-kyu Park

Ye-Rin Jeoung

Mun Hak Lee

Moa Lee

Hyeji Seo

Kye Hwan Lee

Kyoung-Jin Noh

Myung In Lee

A speech recognition model generating device for generating an E2E speech recognition model using calibration correction comprising an acoustic model including a first artificial neural network module using a speech information as input information and using a first text information corresponding to the speech information as output information, a language model comprising a second artificial neural network module using the first text information as input information and outputting a second text information corresponding to the first text information as output information based on the characteristics of the language model, and a E2E speech model generating unit generating a coupling probability distribution based on a first probability distribution information of the acoustic model output by the acoustic model and a second probability distribution information of the language model output by the language model, and generating a E2E speech model based on the coupling probability distribution, wherein the E2E speech model generating unit is generating the E2E speech model based on the corrected acoustic model and the corrected language model after each calibration is performed on the acoustic model and the language model.

Method and apparatus for generating a speech recognition model for generating an E2E speech recognition model using calibration correction

Disclosed are a system and method for automating the design of a sound source separation deep learning model. A method of automating a design of a sound source separation deep learning model, which is performed by a design automation system, may include automatically searching for a combination of hyper parameters of a separation model constructed in a sound source separation deep learning model by using a neural architecture search (NAS) algorithm and reconstructing the sound source separation deep learning model based on the retrieved combination of the hyper parameters of the separation model.

System and method for automating design of sound source separation deep learning model

An apparatus for estimating blood pressure includes: a photoplethysmogram (PPG) sensor configured to measure a PPG signal from an object; a force sensor configured to measure a force signal acting between the object and the PPG sensor; and a processor configured to (i) divide a predetermined blood pressure range into a plurality of classes, (ii) input the measured PPG signal and the measured force signal into a blood pressure estimation model to obtain the probability values for each of the classes, and (iii) estimate blood pressure based on the obtained probability values for the respective classes.

Apparatus and method for estimating blood pressure

Various embodiments relate to a computing device for attention-based joint training with a noise suppression model for a sound event detection (SED) technology that is robust against a noise environment and a method thereof, and are intended to improve SED performance that is robust against a noise environment by using a joint model in which a noise suppression model and an SED model have been jointed. According to various embodiments, an attention scheme and a weight freezing scheme are added to the joint model.

Computing device for attention-based joint training with noise suppression model for sound event detection technology robust against noise environment and method of thereof

A vehicle judder diagnostic method using artificial intelligence applied to a mobile-based GDS according to the present disclosure is characterized in that the mobile-based GDS samples a plurality of sensor signals of a sensor mounted in a vehicle in a vehicle during operation in a judder evaluation mode to quickly, separately diagnose whether the judder phenomenon of the vehicle is a geometric judder or a friction judder by mounting a deep neural network (DNN) model, developed by the trial and error process of a DNN by using the plurality of sensor signals of a test vehicle mounted with a double clutch transmission (DCT), as a judder determination artificial intelligence modelin the mobile-based GDS.

Vehicle judder diagnostic method using artificial intelligence and mobile-based GDS

An apparatus for estimating bio-information is disclosed. The apparatus may include: a pulse wave sensor configured to measure a pulse wave signal from an object; a force sensor configured to obtain a force signal by measuring an external force exerted onto the force sensor; and a processor configured to obtain a first input value, a second input value, and a third input value based on the pulse wave signal and the force signal, to extract a feature vector by inputting the first input value, the second input value, and the third input value into a first neural network model, and to obtain the bio-information by inputting the feature vector into a second neural network model.

Apparatus and method for estimating bio-information

An apparatus for and a method for estimating blood pressure are provided. The apparatus for estimating blood pressure includes: a sensor configured to measure a pulse wave signal from an object; and a processor configured to obtain a mean arterial pressure (MAP) based on the pulse wave signal, configured to classify a phase of the obtained MAP according to at least one classification criterion, and to obtain a systolic blood pressure (SBP) by using an estimation model corresponding to the classified phase of the MAP among estimation models corresponding to respective phases of the MAP.

Presented are a combined learning method and device using a transformed loss function and feature enhancement based on a deep neural network for speaker recognition that is robust in a noisy environment. A combined learning method using a transformed loss function and feature enhancement based on a deep neural network, according to one embodiment, can comprise the steps of: learning a feature enhancement model based on a deep neural network; learning a speaker feature vector extraction model based on the deep neural network; connecting an output layer of the feature enhancement model with an input layer of the speaker feature vector extraction model; and considering the connected feature enhancement model and speaker feature vector extraction model as one mode and performing combined learning for additional learning.

Combined learning method and apparatus using deepening neural network based feature enhancement and modified loss function for speaker recognition robust to noisy environments

Proposed are a deep neural network-based non-autoregressive voice synthesizing method and a system therefor. A deep neural network-based non-autoregressive voice synthesizing system according to an embodiment may comprise: a voice feature vector column synthesizing unit which constitutes a non-recursive deep neural network based on multiple decoders, and gradually produces a voice feature vector column through the multiple decoders from a template including temporal information of a voice; and a voice reconstituting unit which transforms the voice feature vector column into voice data, wherein the voice feature vector column synthesizing unit produces a template input, and produces a voice feature vector column by adding, to the template input, sentence data refined through an attention mechanism.

Deep neural network based non-autoregressive speech synthesizer method and system using multiple decoder

Disclosed are a method and an apparatus for detecting a voice end point by using acoustic and language modeling information to accomplish strong voice recognition. A voice end point detection method according to an embodiment may comprise the steps of: inputting an acoustic feature vector sequence extracted from a microphone input signal into an acoustic embedding extraction unit, a phonemic embedding extraction unit, and a decoder embedding extraction unit, which are based on a recurrent neural network (RNN); combining acoustic embedding, phonemic embedding, and decoder embedding to configure a feature vector by the acoustic embedding extraction unit, the phonemic embedding extraction unit, and the decoder embedding extraction unit; and inputting the combined feature vector into a deep neural network (DNN)-based classifier to detect a voice end point.

Method and apparatus for detecting voice end point using acoustic and language modeling information for robust voice

The present invention relates to a display apparatus that allows a compensated data voltage to be supplied to each pixel by compensating for the data voltage so as to prevent burn-in from occurring in a display panel, a method for compensating a data signal thereof, and a method for generating a deep learning-based compensation model. To implement same, the present invention provides the display apparatus comprising a timing controller having mounted therein the compensation model generated by learning, in a deep learning method, the temperature, time, average brightness, and data voltage for each pixel. Accordingly, the present invention has an effect of preventing burn-in from occurring in each pixel by supplying each pixel with the compensated data voltage generated via the compensation model.

Display apparatus, method for compensating for data signal thereof, and method for generating deep learning-based compensation model

Presented are a combined learning method and device using a transformed loss function and feature enhancement based on a deep neural network for speaker recognition that is robust to a noisy environment. The combined learning method using the transformed loss function and the feature enhancement based on the deep neural network for speaker recognition that is robust to the noisy environment, according to an embodiment, may comprise: a preprocessing step for learning to receive, as an input, a speech signal and remove a noise or reverberation component by using at least one of a beamforming algorithm and a dereverberation algorithm using the deep neural network; a speaker embedding step for learning to classify an utterer from the speech signal, from which a noise or reverberation component has been removed, by using a speaker embedding model based on the deep neural network; and a step for, after connecting a deep neural network model included in at least one of the beamforming algorithm and the dereverberation algorithm and the speaker embedding model, for speaker embedding, based on the deep neural network, performing combined learning by using a loss function.

Method and apparatus for combined learning using feature enhancement based on deep neural network and modified loss function for speaker recognition robust to noisy environments

A device for AI-based vehicle diagnosis using CAN data may include an engine; a vibration sensor mounted in an engine compartment in which the engine is mounted and configured for detecting a vibration signal; and a controller area network (CAN) communicating with one or more of an environmental condition, a vehicle status, an engine status, and an engine control parameter, wherein data preprocessing from the vibration sensor and the CAN is performed to determine features in which correlation between vibration data (dB) exceeding a threshold value of irregular vibrations being generated by the engine and the CAN data is equal to or greater than 90%.

Method of AI-based vehicle diagnosis using CAN data and device thereof

A noise data artificial intelligence learning method for identifying the source of problematic noise may include a noise data pre-conditioning method for identifying the source of problematic noise including: selecting a unit frame for the problematic noise among noises sampled with time; dividing the unit frame into N segments; analyzing frequency characteristic for each segment of the N segments and extracting a frequency component of each segment by applying Log Mel Filter; and outputting a feature parameter as one representative frame by averaging information on the N segments, wherein an artificial intelligence learning by the feature parameter extracted according to a change in time by the noise data pre-conditioning method applies Bidirectional RNN.

Noise data artificial intelligence apparatus and pre-conditioning method for identifying source of problematic noise

Proposed are a generative adversarial network-based speech bandwidth extender and extension method. A generative adversarial network-based speech bandwidth extension method, according to an embodiment, comprises the steps of: extracting feature vectors from a narrowband (NB) signal and a wideband (WB) signal of a speech; estimating the feature vector of the wideband signal from the feature vector of the narrowband signal; and learning a deep neural network classification model for discriminating the estimated feature vector of the wideband signal from the actually extracted feature vector of the wideband signal and the actually extracted feature vector of the narrowband signal.

Generative adversarial network-based speech bandwidth extender and extension method

Growing community of inventors

Seoul, South Korea

Joon-Hyuk Chang