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: 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: 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.

Abstract: 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.

Abstract: A method of encoding/decoding a speech signal and a device for performing the same are provided. The method includes outputting, based on a first input speech signal of a previous timepoint and a second input speech signal of a current timepoint, a predicted signal that predicts the second input speech signal from the first input speech signal and obtaining, based on the second input speech signal and the predicted signal, a residual signal by removing a correlation between the first input speech signal and the second input speech signal from the second input speech signal.

Abstract: A method of encoding/decoding an audio signal and a device for performing the same are provided. The method of encoding an audio signal includes generating, based on the audio signal, a linear prediction coding (LPC) bitstream and a frequency-domain signal of the audio signal, generating, based on the LPC bitstream and the frequency-domain signal, a first residual signal including information on a frequency envelope of the frequency-domain signal, and outputting a second residual signal by processing a first residual signal through one of a plurality of signal processing paths.

Abstract: 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.

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 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.

Abstract: A method and apparatus for encoding/decoding audio signal are provided. The encoding method includes transforming an input audio signal in a time domain into an audio signal in a frequency domain, quantizing energy of a frequency band of the audio signal in the frequency domain, generating a normal signal by normalizing the audio signal in the frequency domain according to quantized energy, obtaining a feature vector including information on the energy of the frequency band based on the normal signal and the input audio signal, quantizing the feature vector, obtaining a scale factor used to scale the normal signal based on the quantized feature vector, quantizing an adjustment signal into which the normal signal has been scaled based on the scale factor, and outputting bitstreams based on the quantized energy, the quantized feature vector, and the quantized adjustment signal.

Abstract: Disclosed are an audio signal encoding/decoding method and an apparatus for performing the same. An audio signal encoding method includes receiving a current frame signal and a reconstructed previous frame signal, generating a predicted current frame signal, based on the current frame signal and the reconstructed previous frame signal, and outputting a reconstructed residual signal, based on the current frame signal and the predicted current frame signal.

Abstract: Provided is an encoding apparatus including a memory configured to store instructions and a processor electrically connected to the memory and configured to execute the instructions, wherein the processor may be configured to perform a plurality of operations, when the instructions are executed by the processor, wherein the plurality of operations may include obtaining an input audio signal, generating an embedded audio signal by embedding signal components of a second frequency band of the input audio signal in a first frequency band of the input audio signal, generating additional information associated with the first frequency band and the second frequency band, generating an encoded audio signal by encoding the embedded audio signal, and formatting the encoded audio signal and the additional information into a bitstream.

Abstract: An audio signal processing method, which is executed by a processor electronically communicating with a deep neural network within a computing system, may comprise: acquiring, by the processor, an input signal before encoding and an output signal after quantization and decoding; calculating, by the processor, a perceptual global loss for a frame corresponding to the input and the output signals; acquiring, by the processor, a plurality of subframes corresponding to the input and output signals by applying a windowing function to the frame of the input and output signals; calculating, by the processor, perceptual local losses for the plurality of subframes corresponding to the input and output signals; and acquiring, by the processor, multi-time scale perceptual loss based on the perceptual global and local losses.

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: 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.

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: Provided is an encoding apparatus including a memory configured to store instructions and a processor electrically connected to the memory and configured to execute the instructions, wherein the processor may be configured to perform a plurality of operations, when the instructions are executed by the processor, wherein the plurality of operations may include obtaining an input audio signal, generating an embedded audio signal by embedding signal components of a second frequency band of the input audio signal in a first frequency band of the input audio signal, generating additional information associated with the first frequency band and the second frequency band, generating an encoded audio signal by encoding the embedded audio signal, and formatting the encoded audio signal and the additional information into a bitstream.