Electronics and Telecommunications Research Institute

Indiana University

Kwangwoon University Industry-academic Collaboration Foundation

Yonsei University

The Korea Development Bank

Gwangju Institute of Science and Technology

National University of Sciences & Technology

Seoul National University

Industry-university Cooperation Foundation Hanyang University

Etri

Seung Kwon Beack

Tae Jin Lee

Dae Young Jang

Jin Woong Kim

Jeong Il Seo

Kyeong Ok Kang

Jong Mo Sung

Jongmo Sung

Yong Ju Lee

Woo-taek Lim

Inseon Jang

Mi Suk Lee

Jin Woo Hong

Keun Woo Choi

Jin Soo Choi

Jae Hyoun Yoo

Kyeongok Kang

Tae Jin Park

Hochong Park

Young-cheol Park

Min Soo Kim

Jeongil Seo

Minje Kim

Chieteuk Ahn

Min Je Kim

In Seon Jang

Hui Yong Kim

Yong Ju Cho

Ji Hun Cha

Ho Chong Park

Seong Yong Lim

Kai Zhen

Myung Seok Ki

Byeongho Cho

Young Ho Jeong

Young-Cheol Park

Rehan Hafiz

Arshad Ali

Jihyun Lee

Ji Hyun Lee

Chanwoo Lee

Hong Goo Kang

Jongwon Shin

Hyoung Soo Lim

Joo Myoung Seok

Hong-Goo Kang

Chan Woo Lee

Young Cheol Park

Min Soo Hahn

Kyu Tae Yang

Sang Bae Chon

Youngju Cheon

Sangwook Han

Soojoong Hwang

Muhammad Murtaza Khan

Jeong Ii Seo

Woo Taek Lim

Bong Ho Lee

Koeng Mo Sung

Han Moon

Jong Won Shin

Ji Sung Yoo

Ashar Rasul

Sung Soo Hwang

Seung Chul Ham

Jong-Won Seok

Seong Dae Kim

Haici Yang

Byeong Ho Cho

Hyung Seob Lim

Wook Joong Kim

Seungmin Shin

Atif Ahmed

Joon Byun

Haris Anis

Hyungseob Lim

Mutahir Latif

Dae Young Jae

Sang-One Kang

Seong-Hyeon Shin

Yunsu Kim

Kwang Ki Kim

A method of encoding a speech signal includes predicting a feature vector of each of a plurality of frames included in the speech signal based on a ground-truth feature vector of a previous frame of each of the plurality of frames, calculating a residual signal corresponding to each of the plurality of frames based on a ground-truth feature vector of each of the plurality of frames and a predicted feature vector of each of the plurality of frames, and generating a bitstring corresponding to each of the plurality of frames by quantizing the residual signal.

Speech coding method and apparatus for performing the same

Disclosed is an apparatus and method for audio encoding/decoding that is robust against coding distortion in a transition section. An audio encoding method includes outputting a frequency domain signal by time-to-frequency (T/F) transform of an input signal, outputting a frequency domain residual signal in which a frequency axis envelope is removed from the frequency domain signal by applying frequency domain noise shaping (FDNS) encoding to the frequency domain signal, outputting a time domain residual signal in which a time axis envelope is removed by performing linear prediction coefficient (LPC) analysis based on the frequency domain residual signal, and quantizing and transmitting the time domain residual signal.

Apparatus and method for audio encoding/decoding robust to transition segment encoding distortion

Disclosed is an apparatus and method for processing a multichannel audio signal. A multichannel audio signal processing method may include: generating an N-channel audio signal of N channels by down-mixing an M-channel audio signal of M channels; and generating a stereo audio signal by performing binaural rendering of the N-channel audio signal.

Apparatus and method for processing multi-channel audio signal

An encoding method, a decoding method, an encoder for performing the encoding method, and a decoder for performing the decoding method are provided. The encoding method includes outputting LP coefficients bitstream and a residual signal by performing an LP analysis on an input signal, outputting a first latent signal obtained by encoding a periodic component of the residual signal, a second latent signal obtained by encoding a non-periodic component of the residual signal, and a weight vector for each of the first latent signal and the second latent signal, using a first neural network module, and outputting a first bitstream obtained by quantizing the first latent signal, a second bitstream obtained by quantizing the second latent signal, and a weight bitstream obtained by quantizing the weight vector, using a quantization module.

Encoding method, decoding method, encoder for performing encoding method, and decoder for performing decoding method

Provided is a method and apparatus for designing and testing an audio codec using quantization based on white noise modeling. A neural network-based audio encoder design method includes generating a quantized latent vector and a reconstructed signal corresponding to an input signal by using a white noise modeling-based quantization process, computing a total loss for training a neural network-based audio codec, based on the input signal, the reconstruction signal, and the quantized latent vector, training the neural network-based audio codec by using the total loss, and validating the trained neural network-based audio codec to select the best neural network-based audio codec.

Method and apparatus for designing and testing audio codec by using white noise modeling

An audio signal encoding and decoding method using a neural network model, and an encoder and decoder for performing the same are disclosed. A method of encoding an audio signal using a neural network model, the method may include identifying an input signal, generating a quantized latent vector by inputting the input signal into a neural network model encoding the input signal, and generating a bitstream corresponding to the quantized latent vector, wherein the neural network model may include i) a feature extraction layer generating a latent vector by extracting a feature of the input signal, ii) a plurality of downsampling blocks downsampling the latent vector, and iii) a plurality of quantization blocks performing quantization of a downsampled latent vector.

Audio signal encoding and decoding method using a neural network model to generate a quantized latent vector, and encoder and decoder for performing the same

Provided is an encoding method according to various example embodiments and an encoder performing the method. The encoding method includes outputting a linear prediction (LP) coefficients bitstream and a residual signal by performing a linear prediction analysis on an input signal, outputting a first latent signal obtained by encoding a periodic component of the residual signal, using a first neural network module, outputting a first bitstream obtained by quantizing the first latent signal, using a quantization module, outputting a second latent signal obtained by encoding an aperiodic component of the residual signal, using the first neural network module, and outputting a second bitstream obtained by quantizing the second latent signal, using the quantization module, wherein the aperiodic component of the residual signal is calculated based on a periodic component of the residual signal decoded from the quantized first latent signal output by de-quantizing the first bitstream.

Methods of encoding and decoding, encoder and decoder performing the methods

An encoding apparatus and a decoding apparatus in a transform between a Modified Discrete Cosine Transform (MDCT)-based coder and a different coder are provided. The encoding apparatus may encode additional information to restore an input signal encoded according to the MDCT-based coding scheme, when switching occurs between the MDCT-based coder and the different coder. Accordingly, an unnecessary bitstream may be prevented from being generated, and minimum additional information may be encoded.

Encoding apparatus and decoding apparatus for transforming between modified discrete cosine transform-based coder and different coder

A method of generating a residual signal performed by an encoder includes identifying an input signal including an audio sample, generating a first residual signal from the input signal using linear predictive coding (LPC), generating a second residual signal having a less information amount than the first residual signal by transforming the first residual signal, transforming the second residual signal into a frequency domain, and generating a third residual signal having a less information amount than the second residual signal from the transformed second residual signal using frequency-domain prediction (FDP) coding.

Method of generating residual signal, and encoder and decoder performing the method

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, executed by a processor for compressing an audio signal in multiple layers, may comprise: (a) restoring, in a highest layer, an input audio signal as a first signal; (b) restoring, in at least one intermediate layer, a signal obtained by subtracting an upsampled signal, which is obtained by upsampling the audio signal restored in the highest layer or an immediately previous intermediate layer, from the input audio signal as a second signal; and (c) restoring, in a lowest layer, a signal obtained by subtracting an upsampled signal, which is obtained by upsampling the audio signal restored in an intermediate layer immediately before the lowest layer, from the input audio signal as a third signal, wherein the first signal, the second signal, and the third signal are combined to output a final restoration audio signal.

Audio signal compression method and apparatus using deep neural network-based multilayer structure and training method thereof

An encoding/decoding apparatus and method for controlling a channel signal is disclosed, wherein the encoding apparatus may include an encoder to encode an object signal, a channel signal, and rendering information for the channel signal, and a bit stream generator to generate, as a bit stream, the encoded object signal, the encoded channel signal, and the encoded rendering information for the channel signal.

Encoding/decoding apparatus for processing channel signal and method

An audio signal encoding and decoding method using a neural network model, a method of training the neural network model, and an encoder and decoder performing the methods are disclosed. The encoding method includes computing the first feature information of an input signal using a recurrent encoding model, computing an output signal from the first feature information using a recurrent decoding model, calculating a residual signal by subtracting the output signal from the input signal, computing the second feature information of the residual signal using a nonrecurrent encoding model, and converting the first feature information and the second feature information to a bitstream.

Methods of encoding and decoding audio signal using neural network model, and devices for performing the methods

Disclosed is a speech processing apparatus and method using a densely connected hybrid neural network. The speech processing method includes inputting a time domain sample of N*1 dimension for an input speech into a densely connected hybrid network; passing the time domain sample through a plurality of dense blocks in a densely connected hybrid network; reshaping the time domain samples into M subframes by passing the time domain samples through the plurality of dense blocks; inputting the M subframes into gated recurrent unit (GRU) components of N/M-dimension; outputting clean speech from which noise is removed from the input speech by passing the M subframes through GRU components.

Apparatus and method for speech processing using a densely connected hybrid neural network

Growing community of inventors

Daejeon, South Korea

Seung Kwon Beack