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 a method of encoding and decoding an audio signal using linear predictive coding (LPC) and an encoder and a decoder that perform the method. The method of encoding an audio signal to be performed by the encoder includes identifying a time-domain audio signal block-wise, quantizing a linear prediction coefficient obtained from a block of the audio signal through the LPC, generating an envelope based on the quantized linear prediction coefficient, extracting a residual signal based on the envelope and a result of converting the block into a frequency domain, grouping the residual signal by each sub-band and determining a scale factor for quantizing the grouped residual signal, quantizing the residual signal using the scale factor, and converting the quantized residual signal and the quantized linear prediction coefficient into a bitstream and transmitting the bitstream to a decoder.

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 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.

Abstract: A method of predicting a channel parameter of an original signal from a downmix signal is disclosed. The method may include generating an input feature map to be used to predict a channel parameter of the original signal based on a downmix signal of an original signal, determining an output feature map including a predicted parameter to be used to predict the channel parameter by applying the input feature map to a neural network, generating a label map including information associated with the channel parameter of the original signal, and predicting the channel parameter of the original signal by comparing the output feature map and the label map.

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 a quantizing method for a latent vector and a computing device for performing the quantization method. A quantizing method of a latent vector includes performing information shaping on the latent vector resulting from reduction in a dimension of an input signal using a target neural network; clamping a residual signal of the latent vector derived based on the information shaping; performing resealing on the clamped residual signal; and performing quantization on the resealed residual signal.

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: An audio signal encoding/decoding device and method using a filter bank is disclosed. The audio signal encoding method includes generating a plurality of first audio signals by performing filtering on an input audio signal using an analysis filter bank, generating a plurality of second audio signals by performing downsampling on the first audio signals, and outputting a bitstream by encoding and quantizing the second audio signals.

Abstract: Disclosed are a method for coding a residual signal of LPC coefficients based on collaborative quantization and a computing device for performing the method. The residual signal coding method includes: generating encoded LPC coefficients and LPC residual signals by performing LPC analysis and quantization on an input speech; Determining a predicted LPC residual signal by applying the LPC residual signal to cross module residual learning; Performing LPC synthesis using the coded LPC coefficients and the predicted LPC residual signal; It may include the step of determining an output speech that is a synthesized output according to a result of performing the LPC synthesis.

Abstract: Provided are an audio encoding method, an audio decoding method, an audio encoding apparatus, and an audio decoding apparatus using dynamic model parameters. The audio encoding method using dynamic model parameters may use dynamic model parameters corresponding to each of the levels of the encoding network when reducing the dimension of an audio signal in the encoding network. In addition, the audio decoding method using the dynamic model parameter may use a dynamic model parameter corresponding to each of the levels of the decoding network when extending the dimension of an audio signal in an encoding network.

Abstract: Disclosed are a method of encoding a high band of an audio, a method of decoding a high band of an audio, and an encoder and a decoder for performing the methods. The method of decoding a high band of an audio, the method performed by a decoder, includes identifying a parameter extracted through a first neural network, identifying side information extracted through a second neural network, and restoring a high band of an audio by applying the parameter and the side information to a third neural network.

Abstract: Disclosed is a method of processing a residual signal for audio coding and an audio coding apparatus. The method learns a feature map of a reference signal through a residual signal learning engine including a convolutional layer and a neural network and performs learning based on a result obtained by mapping a node of an output layer of the neural network and a quantization level of index of the residual signal.

Abstract: Provided is an apparatus and method for encoding/decoding audio based on a block. A method of encoding an audio signal may include dividing each of frame of input signal that constitute an audio signal into a plurality of subframes; transforming the subframes to a frequency domain; determining a two-dimensional (2D) intra block using the subframes transformed to the frequency domain; and encoding the 2D intra block. The 2D intra block may be a block that two-dimensionally displays frequency coefficients of the subframes transformed to the frequency domain using a time and a frequency.

Abstract: An inventive concept relates to an audio coding method to which CNN-based frequency spectrum recovery is applied. An inventive concept transmits a part of frequency spectral coefficients generated in transform coding to a decoder and the decoder recovers the frequency spectral coefficient not transmitted. Furthermore, the signs of frequency spectral coefficient are transmitted from an encoder to the decoder depending on a sign transmission rule.

Abstract: Disclosed are an audio signal encoding method and audio signal decoding method, and an encoder and decoder performing the same. The audio signal encoding method includes applying an audio signal to a training model including N autoencoders provided in a cascade structure, encoding an output result derived through the training model, and generating a bitstream with respect to the audio signal based on the encoded output result.

Abstract: Disclosed are an audio signal encoding method and device, and an audio signal decoding method and device. The encoding method includes transforming an original test signal of a time domain being an audio signal into a frequency domain, binarizing a coefficient of the original test signal of the frequency domain, performing an encoding layer feedforward using the binarized coefficient and a training model parameter derived through a training process, and performing an entropy encoding based on a result of performing the encoding layer feedforward.

Abstract: A method of predicting a channel parameter of an original signal from a downmix signal is disclosed. The method may include generating an input feature map to be used to predict a channel parameter of the original signal based on a downmix signal of an original signal, determining an output feature map including a predicted parameter to be used to predict the channel parameter by applying the input feature map to a neural network, generating a label map including information associated with the channel parameter of the original signal, and predicting the channel parameter of the original signal by comparing the output feature map and the label map.

Abstract: Provided is a training method of a neural network that is applied to an audio signal encoding method using an audio signal encoding apparatus, the training method including generating a masking threshold of a first audio signal before training is performed, calculating a weight matrix to be applied to a frequency component of the first audio signal based on the masking threshold, generating a weighted error function obtained by correcting a preset error function using the weight matrix, and generating a second audio signal by applying a parameter learned using the weighted error function to the first audio signal.

Abstract: Provided is an apparatus and method for encoding/decoding audio based on a block. A method of encoding an audio signal may include dividing each of frame of input signal that constitute an audio signal into a plurality of subframes; transforming the subframes to a frequency domain; determining a two-dimensional (2D) intra block using the subframes transformed to the frequency domain; and encoding the 2D intra block. The 2D intra block may be a block that two-dimensionally displays frequency coefficients of the subframes transformed to the frequency domain using a time and a frequency.