Abstract: Provided are an encoding method, an encoding device, a decoding method, and a decoding device using a scalar quantization and a vector quantization. The encoding method includes converting an input signal of a time domain into a frequency domain, generating a first residual signal from an input signal of a frequency domain by using a scale factor, performing a scalar quantization of the first residual signal, generating a second residual signal from the scalar-quantized first residual signal, performing a lossless encoding of the scalar-quantized first residual signal, performing a vector quantization of the second residual signal, and transmitting a bitstream including the lossless-encoded first residual signal and the vector-quantized second residual signal.

Abstract: A complex number quantization-based audio signal encoding method may comprise: estimating a scale factor for each subband of an input audio signal; performing complex magnitude scaling for each subband based on the scale factor; and performing polar quantization on a complex frequency coefficient for each subband, wherein the performing the polar quantization for each subband comprises applying two or more different magnitude quantization techniques based on the magnitude of the complex frequency coefficient scaled for each subband.

Abstract: An audio signal encoding/decoding method and an apparatus for performing the same are disclosed. The audio signal encoding method includes obtaining a full-band input signal, extracting a first feature vector corresponding to a first sub-band signal and a second feature vector corresponding to a second sub-band signal using an encoder neural network including a plurality of encoding layers, generating a first code vector corresponding to the first feature vector and a second code vector corresponding to the second feature vector by compressing the first feature vector and the second feature vector, and generating a bitstream by quantizing the first code vector and the second code vector.

Abstract: A generative adversarial network-based audio signal generation model for generating a high quality audio signal may comprise: a generator generating an audio signal with an external input; a harmonic-percussive separation model separating the generated audio signal into a harmonic component signal and a percussive component signal; and at least one discriminator evaluating whether each of the harmonic component signal and the percussive component signal is real or fake.

Abstract: Provided are an apparatus for encoding an audio signal and a method of an operation thereof. An audio signal encoding method includes obtaining quantized linear prediction (LP) coefficients by performing a linear predictive coding (LPC) analysis and quantization on an input audio signal, generating a reference signal by applying discrete Fourier transform (DFT) to the input audio signal, obtaining LP residual coefficients from the reference signal, scaling magnitudes of the LP residual coefficients using the quantized LP coefficients and the reference signal, and quantizing phases of the LP residual coefficients and the scaled magnitudes of the LP residual coefficients.

Abstract: The encoding method includes computing the first feature information of an input signal using a recurrent encoding model, quantizing the first feature information and producing the first feature bitstream, computing the first output signal from the quantized first feature information using a recurrent decoding model, computing the second feature information of the input signal using a nonrecurrent encoding model, quantizing the second feature information and producing the second feature bitstream, computing the second output signal from the quantized second feature information using a nonrecurrent decoding model, determining an encoding mode based on the input signal, the first and second output signals, and the first and second feature bitstreams, and outputting an overall bitstream by multiplexing an encoding mode bit and one of the first feature bitstream and the second feature bitstream depending on the encoding mode.

Abstract: A method for encoding an input signal using N flow blocks (N is a natural number greater than or equal to 2) and (N?1) split block(s), which is performed by a processor, may comprise: transmitting, by a k-th flow block (k is a natural number greater than or equal to 1 and less than or equal to N?1) among the N flow blocks, a k-th transformation signal obtained by transforming a received signal into a latent representation to a k-th split block among the (N?1) split block(s); splitting, by the k-th split block, the k-th transformation signal by a predetermined ratio, into a first split signal and a second split signal; transmitting, by the k-th split block, the first split signal to a (k+1)-th flow block; and quantizing a signal transformed by an N-th flow block and the second split signals using a quantization block.

Abstract: 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.

Abstract: A method of extending a bandwidth of an audio signal is provided. The method includes obtaining low-band patches in which a low-band spectrum corresponding to a low-band signal is segmented, generating a key parameter and a value parameter based on the low-band patches, predicting high-band patches from the low-band patches based on the key parameter and the value parameter, and generating a full-band spectrum based on the predicted high-band patches and the low-band spectrum.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and device for encoding/decoding an audio signal based on dequantization through potential diffusion are provided. The method of decoding an audio signal includes obtaining a discrete latent vector in which a speech signal is quantized and based on the discrete latent vector, outputting a continuous latent vector in which the discrete latent vector is dequantized.

Abstract: Disclosed are a device and method for audio signal processing. The audio signal processing device according to an embodiment includes a receiver configured to receive a bitstream corresponding to a compressed audio signal and a processor. The processor may be configured to generate a real restoration signal or a complex restoration signal by performing inverse quantization on real data of the bitstream or complex data of the bitstream, generate a result of real Frequency Domain Noise Shaping (FDNS) synthesis or a result of complex FDNS synthesis by performing FDNS synthesis on the real restoration signal or the complex restoration signal, and generate a restored audio signal by performing frequency-to-time transform on the result of the real FDNS synthesis or the result of the complex FDNS synthesis.

Abstract: A method and apparatus for encoding/decoding a neural network-based personalized speech are provided. The method includes outputting a first bit stream in which an input speech signal is encrypted, based on the input speech signal, and outputting a second bit stream in which speaker information of the input speech signal is encrypted, based on the input speech signal.

Abstract: 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 MIDCT-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.

Abstract: 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.

Abstract: 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.

Abstract: 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.