Abstract: A method and apparatus for processing an audio signal are disclosed. According to an example embodiment, a method of processing an audio signal may include acquiring a final audio signal for an initial audio signal using a plurality of neural network models generating output audio signals by encoding and decoding input audio signals, calculating a difference between the initial audio signal and the final audio signal in a time domain, converting the initial audio signal and the final audio signal into Mel-spectra, calculating a difference between the Mel-spectra of the initial audio signal and the final audio signal in a frequency domain, training the plurality of neural network models based on results calculated in the time domain and the frequency domain, and generating a new final audio signal distinguished from the final audio signal from the initial audio signal using the trained neural network models.

Abstract: Disclosed are methods of encoding and decoding an audio signal using side information, and an encoder and a decoder for performing the methods. The method of encoding an audio signal using side information includes identifying an input signal, the input signal being an original audio signal, extracting side information from the input signal using a learning model trained to extract side information from a feature vector of the input signal, encoding the input signal, and generating a bitstream by combining the encoded input signal and the side information.

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: 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 method and an encoding device using a complex signal and a decoding method and a decoding device using a complex signal are provided. The encoding method includes converting a first channel signal and a second channel signal constituting an audio signal corresponding to a stereo signal from a real domain to a complex domain, determining one of a sum operation, a difference operation, and a bypass operation to be performed on the second channel signal converted to the complex domain, determining a complex spatial cue according to the determined operation, converting a residual signal for the second channel signal to a real domain using the complex spatial cue, converting the first channel signal to a real domain, encoding the first channel signal converted to the real domain, and encoding the residual signal for the second channel signal converted to the real domain.

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: 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: 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.

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 method of encoding an audio signal and an encoder and a method of decoding an audio signal and a decoder are provided. The method of encoding an audio signal includes outputting a decoded signal by using a bitstream that encodes an audio signal, separating the decoded signal into a low-band signal and a high-band signal by using a sound source separator, upsampling the low-band signal, upsampling the high-band signal, and restoring the audio signal by synthesizing the upsampled low-band signal with the upsampled high-band signal, wherein the bitstream is generated by encoding a superimposed signal in which a signal in a high frequency band of the audio signal is superimposed on a low frequency band of the audio signal.

Abstract: An audio signal encoding and decoding method using a learning model, a training method of the learning model, and an encoder and decoder that perform the method, are disclosed. The audio signal decoding method may include extracting a first residual signal and a first linear prediction coefficient by decoding a bitstream received from an encoder, generating a first audio signal from the first residual signal using the first linear prediction coefficient, generating a second linear prediction coefficients and a second residual signal from the first audio signal, obtaining a third linear prediction coefficient by inputting the second linear prediction coefficient into a trained learning model, and generating a second audio signal from the second residual signal using the third linear prediction coefficient.

Abstract: Disclosed is a binaural rendering method and apparatus for decoding a multichannel audio signal. The binaural rendering method may include: extracting an early reflection component and a late reverberation component from a binaural filter; generating a stereo audio signal by performing binaural rendering of a multichannel audio signal base on the early reflection component; and applying the late reverberation component to the generated stereo audio signal.

Abstract: Methods of encoding and decoding a speech signal using a neural network model that recognizes sound sources, and encoding and decoding apparatuses for performing the methods are provided. A method of encoding a speech signal includes identifying an input signal for a plurality of sound sources; generating a latent signal by encoding the input signal; obtaining a plurality of sound source signals by separating the latent signal for each of the plurality of sound sources; determining a number of bits used for quantization of each of the plurality of sound source signals according to a type of each of the plurality of sound sources; quantizing each of the plurality of sound source signals based on the determined number of bits; and generating a bitstream by combining the plurality of quantized sound source signals.

Abstract: Disclosed are methods of encoding and decoding an audio signal, and an encoder and a decoder for performing the methods. The method of encoding an audio signal includes identifying an input signal corresponding to a low frequency band of the audio signal, windowing the input signal, generating a first latent vector by inputting the windowed input signal to a first encoding model, transforming the windowed input signal into a frequency domain, generating a second latent vector by inputting the transformed input signal to a second encoding model, generating a final latent vector by combining the first latent vector and the second latent vector, and generating a bitstream corresponding to the final latent vector.

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 audio signal encoding method performed by an encoder includes identifying an audio signal of a time domain in units of a block, generating a combined block by combining i) a current original block of the audio signal and ii) a previous original block chronologically adjacent to the current original block, extracting a first residual signal of a frequency domain from the combined block using linear predictive coding of a time domain, overlapping chronologically adjacent first residual signals among first residual signals converted into a time domain, and quantizing a second residual signal of a time domain extracted from the overlapped first residual signal by converting the second residual signal of the time domain into a frequency domain using linear predictive coding of a frequency domain.

Abstract: Disclosed is an apparatus and method for encoding/decoding an audio signal using information of a previous frame. An audio signal encoding method includes: generating a current latent vector by reducing dimension of a current frame of an audio signal; generating a concatenation vector by concatenating a previous latent vector generated by reducing dimension of a previous frame of the audio signal with the current latent vector; and encoding and quantizing the concatenation vector.

Abstract: Disclosed are a method of encoding and decoding an audio signal and an encoder and a decoder performing the method. The method of encoding an audio signal includes identifying an input signal, and generating a bitstring of each encoding layer by applying, to the input signal, an encoding model including a plurality of successive encoding layers that encodes the input signal, in which a current encoding layer among the encoding layers is trained to generate a bitstring of the current encoding layer by encoding an encoded signal which is a signal encoded in a previous encoding layer and quantizing an encoded signal which is a signal encoded in the current encoding layer.

Abstract: An audio encoding/decoding apparatus and method using vector quantized residual error features are disclosed. An audio signal encoding method includes outputting a bitstream of a main codec by encoding an original signal, decoding the bitstream of the main codec, determining a residual error feature vector from a feature vector of a decoded signal and a feature vector of the original signal, and outputting a bitstream of additional information by encoding the residual error feature vector.

Abstract: An audio signal encoding method performed by an encoder includes identifying a time-domain audio signal in a unit of blocks, quantizing a linear prediction coefficient extracted from a combined block in which a current original block of the audio signal and a previous original block chronologically adjacent to the current original block using frequency-domain linear predictive coding (LPC), generating a temporal envelope by dequantizing the quantized linear prediction coefficient, extracting a residual signal from the combined block based on the temporal envelope, quantizing the residual signal by one of time-domain quantization and frequency-domain quantization, and transforming the quantized residual signal and the quantized linear prediction coefficient into a bitstream.