Electronics and Telecommunications Research Institute

Kwangwoon University Industry-academic Collaboration Foundation

Yonsei University

Wilus Institute of Standards and Technology Inc.

Indiana University

Industry-University Cooperation Foundation Hanyang University

University of Southampton

Jeongil Seo

Kyeongok Kang

Tae Jin Lee

Dae Young Jang

Jin Woo Hong

Young-cheol Park

Min Soo Kim

Chieteuk Ahn

Hochong Park

Seungkwon Beack

Jin-Woo Hong

Seung-Kwon Baek

Seung Kwon Beack

Ho Chong Park

Taegyu Lee

Hyunoh Oh

Yongju Lee

Min Je Kim

Youngcheol Park

Daehee Youn

Daeyoung Jang

Jinwoong Kim

Kwangki Kim

Minsoo Hahn

Jin Woong Kim

Hwan Shim

Seung Kwon Baek

Jae-hyoun Yoo

Inseon Jang

Young-Cheol Park

Young Cheol Park

David Crandall

Soon-heung Jung

Md Alimoor Reza

Hui Yong Kim

Jin Soo Choi

Yong Ju Lee

Hyun Oh Oh

Hyun Cheol Kim

Soon Heung Jung

Yong Ju Cho

Philip Arthur Nelson

Hyunjoo Chung

Joo Myoung Seok

Seong Yong Lim

Eung Don Lee

Sangwoo Ahn

Hyun-joo Chung

Hee Kyung Lee

Seung Jun Yang

Injae Lee

Filippo Maria Fazi

SeungChul Ham

Jinwoo Hong

Koen-Mo Sung

Dae Young Jae

Sang-One Kang

Nikhil Thakurdesai

Jagpreet Singh Chawla

Anuj Godase

Minje Kim

Seungchul Ham

A Unified Speech and Audio Codec (USAC) that may process a window sequence based on mode switching is provided. The USAC may perform encoding or decoding by overlapping between frames based on a folding point when mode switching occurs. The USAC may process different window sequences for each situation to perform encoding or decoding, and thereby may improve a coding efficiency.

Unified speech/audio codec (USAC) processing windows sequence based mode switching

A depth estimation method and apparatus using a learning model are disclosed. A depth information estimation method may include identifying a plurality of image data, generating feature maps of the plurality of image data respectively, generating a cost volume using the feature maps, generating normalized cost volumes in different sizes by normalizing the cost volume, estimating disparity information from the normalized cost volumes, and generating depth information using the estimated disparity information.

Depth estimation method and apparatus using learning model

Provided is an encoding apparatus for integrally encoding and decoding a speech signal and a audio signal, and may include: an input signal analyzer to analyze a characteristic of an input signal; a stereo encoder to down mix the input signal to a mono signal when the input signal is a stereo signal, and to extract stereo sound image information; a frequency band expander to expand a frequency band of the input signal; a sampling rate converter to convert a sampling rate; a speech signal encoder to encode the input signal using a speech encoding module when the input signal is a speech characteristics signal; a audio signal encoder to encode the input signal using a audio encoding module when the input signal is a audio characteristic signal; and a bitstream generator to generate a bitstream.

Apparatus for encoding and decoding of integrated speech and audio

The present invention relates to a method and an apparatus for binaural rendering an audio signal using variable order filtering in frequency domain. To this end, provided are a method for processing an audio signal including: receiving an input audio signal; receiving a set of truncated subband filter coefficients for filtering each subband signal of the input audio signal, the set of truncated subband filter coefficients being constituted by one or more FFT filter coefficients generated by performing FFT by a predetermined block size; generating at least one subframe for each subband; generating at least one filtered subframe for each subband; performing inverse FFT on the filtered subframe for each subband; and generating a filtered subband signal by overlap-adding the transformed subframe for each subband and an apparatus for processing an audio signal using the same.

Method and apparatus for binaural rendering audio signal using variable order filtering in frequency domain

Disclosed is an LPC residual signal encoding/decoding apparatus of an MDCT based unified voice and audio encoding device. The LPC residual signal encoding apparatus analyzes a property of an input signal, selects an encoding method of an LPC filtered signal, and encode the LPC residual signal based on one of a real filterbank, a complex filterbank, and an algebraic code excited linear prediction (ACELP).

LPC residual signal encoding/decoding apparatus of modified discrete cosine transform (MDCT)-based unified voice/audio encoding device

A method and apparatus for correcting an error in depth information estimated from a two-dimensional (2D) image are disclosed. The method includes diagnosing an error in depth information by inputting a color image and depth information estimated using the color image to a depth error detection network, and determining enhanced depth information by maintaining or correcting the depth information based on the diagnosed error.

Method and apparatus for correcting error in depth information estimated from 2D image

Provided are an apparatus and a method for integrally encoding and decoding a speech signal and a audio signal. The encoding apparatus may include: an input signal analyzer to analyze a characteristic of an input signal; a first conversion encoder to convert the input signal to a frequency domain signal, and to encode the input signal when the input signal is a audio characteristic signal; a Linear Predictive Coding (LPC) encoder to perform LPC encoding of the input signal when the input signal is a speech characteristic signal; and a bitstream generator to generate a bitstream using an output.

Apparatus and method for encoding and decoding of integrated speech and audio utilizing a band expander with a spectral band replication (SBR) to output the SBR to either time or transform domain encoding  according to the input signal

A multi-object audio encoding and decoding apparatus supporting a post downmix signal may be provided. The multi-object audio encoding apparatus may include: an object information extraction and downmix generation unit to generate object information and a downmix signal from input object signals; a parameter determination unit to determine a downmix information parameter using the extracted downmix signal and the post downmix signal; and a bitstream generation unit to combine the object information and the downmix information parameter, and to generate an object bitstream.

Multi-object audio encoding and decoding apparatus supporting post down-mix signal

Apparatus and method for encoding and decoding of integrated speech and audio utilizing a band expander with a spectral band replication (SBR) to output the SBR to either time or transform domain encoding according to the input signal characteristic

Growing community of inventors

Daejeon, South Korea

Jeongil Seo