Lg Electronics Inc.

Chungbuk National University Industry-academic Cooperation Foundation

Yonsei University

Chungbuk National University Industry Academic

Hyejeong Jeon

Gyuhyeok Jeong

Lagyoung Kim

Ingyu Kang

Jaehong Kim

Hyoeun Kim

Byungsuk Lee

Hangil Jeong

Heeyeon Choi

Daehwan Kim

Younghan Lee

Taeho Lee

Jongwoo Han

Kamin Lee

Byounggi Lee

Kibong Hong

Insung Lee

Heungkyu Lee

Changheon Lee

Zhigang Piao

Jongha Lim

Sanghyeon Moon

Doyoung Lee

Yoojin Choi

Soungmin Im

Dami Choe

Yongsuk Lee

Seungah Chae

Hyunseok Shin

Beomsuk Moon

Gunho Song

Sangwhan Moon

Minki Lee

Honggoo Kang

Yeonjee Jung

Taesu Kim

Seungil Kim

The present disclosure relates to an artificial intelligence apparatus capable of classifying and detecting an item of an unseen class having high visual similarity to an item of a seen class that has been learned and known in advance, and a method for detecting an unseen class items thereof, and when the item image is input, generate encoded data by encoding the item image, generate decoded data by decoding the encoded data using a codebook of a plurality of codebooks in which encoded data for each item class is stored, corresponding to a pre-learned item class, and detect the unknown item by classifying the class of the item image based on the generated decoded data.

Artificial intelligence apparatus and method for detecting unseen class items thereof

According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, an outer door, one or more cameras provided in the outer door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment through the one or more cameras, obtain a food identification item from the photographed internal image, determine whether the obtained food identification item is stored in the local DB, and when the food identification item is not stored in the local DB, determine whether the obtained food identification item is stored in the global DB.

Refrigerator

The present disclosure relates to an artificial intelligence apparatus capable of providing a user manual corresponding to an abnormality diagnosis by diagnosing an abnormality of a target device inquired by a user, and a method for providing a target device manual thereof, and may be configured to, when receiving a user inquiry from the user terminal through the communication unit, analyze the user inquiry to set a diagnosis range of a target device, perform a diagnosis corresponding to the user inquiry within the set diagnosis range of the target device, generate a user manual for the performed diagnosis result, and control the communication unit to transmit the generated a user manual to the user terminal.

Artificial intelligence apparatus and method for providing target device manual thereof

Provided is an artificial intelligence air conditioner for calibrating sensor data. The artificial intelligence air conditioner includes a sensor unit configured to acquire sensor data; a communication unit configured to receive at least one of external sensor data or environment information from at least one of an external air conditioner or an internet of things (IoT) device; and a processor. The processor is configured to generate estimated sensor data corresponding to the sensor unit using a sensor data estimation model, the received external sensor data and the received environment information, determine whether the acquired sensor data is abnormal using the generated estimated sensor data, perform an air conditioning function using the acquired sensor data if the acquired sensor data is determined as normal, and perform the air conditioning function using the generated estimated sensor data if the acquired sensor data is determined as abnormal.

Artificial intelligence air conditioner and method for calibrating sensor data of air conditioner

An artificial intelligence apparatus for generating training data includes a memory configured to store a target artificial intelligence model, and a processor configured to receive sensor data, determine whether the received sensor data is irrelevant to a learning of the target artificial intelligence model, determine whether the received sensor data is useful for the learning if the received sensor data is determined to be relevant to the learning, extract a label from the received sensor data by using a label extractor if the received sensor data is determined to be useful for the learning, determine a confidence level of the extracted label, and generate training data including the received sensor data and the extracted label if the determined confidence level exceeds a first reference value.

Artificial intelligence apparatus for generating training data, artificial intelligence server, and method for the same

According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, a door, one or more cameras provided in the door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment, obtain one or more food identification items from the photographed internal image, and if the obtained one or more food identification items are stored in the local DB, recognize one or more product names of the obtained one or more food identification items as product names of the food identification items stored in the local DB.

Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.

Laundry scheduling apparatus and method

A laundry scheduling device according to an embodiment of the present invention includes an input interface that receives speech including a plurality of words respectively representing a plurality of laundry items from a use, and a processor that acquires one or more features represented by each of the plurality of words by inputting speech data corresponding to the received speech to a learning model and performing word embedding, and generates a laundry schedule of the plurality of laundry items based on the one or more features represented by each of the plurality of words.

Laundry scheduling device

An artificial intelligence apparatus for recognizing a user includes a camera, and a process configured to receive, via the camera, image data including a recognition target object, generate recognition information corresponding to the recognition target object from the received image data, calculate a confidence level of the generated recognition information, determine whether the calculated confidence level is greater than a reference value, if the calculated confidence level is greater than the reference value, perform a control corresponding to the generated recognition information, and if the calculated confidence level is not greater than the reference value, provide a feedback for the object recognition.

Artificial intelligence apparatus for recognizing user from image data and method for the same

An electronic device including a voice obtaining microphone configured to receive a voice signal including a noise at a first level; a noise obtaining microphone configured to receive a voice signal including a noise at a second level higher than the first level; and a controller configured to estimate the noise at the second level from the voice signal received by the noise obtaining microphone, remove the noise at the first level from the voice signal received by the voice obtaining microphone based on the estimated noise, and perform an operation corresponding to the voice signal having the noise at the first level eliminated therefrom.

Electronic device for speech recognition

The present invention relates to a method for processing an audio signal, comprising: a step of performing a frequency conversion process on an audio signal to obtain a plurality of frequency transform coefficients; a step of selecting either a general mode or a non-general mode, on the basis of a pulse ratio, for the frequency transform coefficients having a high frequency band from among the plurality of frequency transform coefficients; and a step of performing, if the non-general mode is selected, the following steps: extracting a predetermined number of pulses from the frequency transform coefficients having the high frequency band, and generating pulse information; generating an original noise signal from the frequency transform coefficients having the high frequency band, excluding the pulses; generating a reference noise signal using the frequency transform coefficient having a low frequency band from among the plurality of frequency transform coefficients; and generating noise position information and noise energy information using the original noise signal and the reference noise signal.

Method and apparatus for processing an audio signal

The present invention relates to a method for encoding a voice signal, a method for decoding a voice signal, and an apparatus using the same. The method for encoding the voice signal according to the present invention, includes the steps of: 

Method for encoding voice signal, method for decoding voice signal, and apparatus using same

The present invention relates to a frame loss recovering method, an audio decoding method, and an apparatus using the method. A method of recovering a frame loss of an audio signal according to the present invention includes: grouping transform coefficients of at least one frame into a predetermined number of bands among previous frames of a current frame; deriving an attenuation constant according to a tonality of the bands; and recovering transform coefficients of the current frame by applying the attenuation constant to the previous frame of the current frame.

Frame loss recovering method, and audio decoding method and device using same

A method for controlling a voice tracking apparatus according to an embodiment of the present invention includes the steps of: tracking a sound source of a voice signal generated from the outside; turning an image capturing unit of the voice tracking apparatus toward the location of the tracked sound source; and beamforming the voice signal of the sound source through a voice input unit mounted on the image capturing unit.

Voice tracking apparatus and control method therefor

The present invention relates to a method and apparatus for processing a voice signal, and the voice signal encoding method according to the present invention comprises the steps of: generating transform coefficients of sine wave components forming an input voice signal by transforming the sine wave components; determining transform coefficients to be encoded from the generated transform coefficients; and transmitting indication information indicating the determined transform coefficients, wherein the indication information may include position information, magnitude information, and sign information of the transform coefficients.

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Seoul, South Korea

Hyejeong Jeon