Electronics and Telecommunications Research Institute

Kwangwoon University Industry-academic Collaboration Foundation

Indiana University

Yonsei University

The Korea Development Bank

Gwangju Institute of Science and Technology

Nippon Hoso' Kyokai

Kyonggi University Industry & Academia Cooperation Foundation

Chung Ang University Industry Academic Cooperation

Etri

Tae Jin Lee

Seung Kwon Beack

Dae Young Jang

Kyeongok Kang

Jong Mo Sung

Jin Woong Kim

Kyeong Ok Kang

Jeongil Seo

Young-cheol Park

Jongmo Sung

Yong Ju Lee

Jin Woo Hong

Jeong Il Seo

Min Soo Kim

Woo-taek Lim

Hochong Park

Chieteuk Ahn

Inseon Jang

Jin Soo Choi

Jae Hyoun Yoo

Mi Suk Lee

Jae-hyoun Yoo

Keun Woo Choi

Ho Chong Park

Jin-Woo Hong

Seung-Kwon Baek

Young Ho Jeong

Min Je Kim

Tae Jin Park

Seungkwon Beack

Minje Kim

Hui Yong Kim

Seok Pil Lee

Chie Teuk Ahn

In Seon Jang

Young Cheol Park

Kai Zhen

Byeongho Cho

Seung Kwon Baek

Kaoru Watanabe

Soo Young Park

Sang Won Suh

Youngju Cheon

Soojoong Hwang

Sangwook Han

Satoshi Oode

Ikuko Sawaya

Jihyun Lee

Ji Hyun Lee

Sang Wook Kim

Hong Goo Kang

Jongwon Shin

Hyoung Soo Lim

Hong-Goo Kang

Young-Cheol Park

Chan Woo Lee

Kyu Tae Yang

Bong Ho Lee

Woo Taek Lim

Jong Won Shin

Chanwoo Lee

Seok Jin Lee

Seung Chul Ham

Jeong Ii Seo

Jong-Won Seok

Hyung Seob Lim

Hyungseob Lim

SeungChul Ham

Joon Byun

Seungmin Shin

Haici Yang

Byeong Ho Cho

Seong-Hyeon Shin

Yunsu Kim

Seung Kwon Baeck

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 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

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

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

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 and apparatus for rendering an object-based audio signal considering an obstacle are disclosed. A method for rendering an object-based audio signal according to an example embodiment, the method includes identifying an object-based input signal and metadata for the input signal, generating a binaural filter based on the metadata using a binaural room impulse response (BRIR), determining, based on the metadata, whether an obstacle is present between a listener and an object, modifying the generated binaural filter when it is determined that the obstacle is present, and generating a rendered output signal by convolving the modified binaural filter and the input signal.

Method and apparatus for rendering object-based audio signal considering obstacle

Disclosed is a method and apparatus for label encoding in a multi-sound event interval. The method includes identifying an event interval in which a plurality of sound events occurs in a sound signal, separating a sound source into sound event signals corresponding to each sound event by performing sound source separation on the event interval, determining energy information for each of the sound event signals, and performing label encoding based on the energy information.

Method and apparatus for label encoding in polyphonic sound event intervals

Disclosed are a training method for a learning model for recognizing an acoustic signal, a method of recognizing an acoustic signal using the learning model, and devices for performing the methods. The method of recognizing an acoustic signal using a learning model includes identifying an acoustic signal including an acoustic event or acoustic scene, determining an acoustic feature of the acoustic signal, dividing the determined acoustic feature for each of a plurality of frequency band intervals, and determining the acoustic event or acoustic scene included in the acoustic signal by inputting the divided acoustic features to a trained learning model.

Training and learning model for recognizing acoustic signal

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

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Daejeon, South Korea

Tae Jin Lee