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: 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.

Abstract: An audio signal encoding and decoding method using a neural network model, a method of training the neural network model, and an encoder and decoder performing the methods are disclosed. The encoding method includes computing the first feature information of an input signal using a recurrent encoding model, computing an output signal from the first feature information using a recurrent decoding model, calculating a residual signal by subtracting the output signal from the input signal, computing the second feature information of the residual signal using a nonrecurrent encoding model, and converting the first feature information and the second feature information to a 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: 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: 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: The present invention discloses an encoding apparatus using a Discrete Cosine Transform (DCT) scanning, which includes a mode selection means for selecting an optimal mode for intra prediction; an intra prediction means for performing intra prediction onto video inputted based on the mode selected in the mode selection means; a DCT and quantization means for performing DCT and quantization onto residual coefficients of a block outputted from the intra prediction means; and an entropy encoding means for performing entropy encoding onto DCT coefficients acquired from the DCT and quantization by using a scanning mode decided based on pixel similarity of the residual coefficients.

Abstract: The present invention discloses an encoding apparatus using a Discrete Cosine Transform (DCT) scanning, which includes a mode selection means for selecting an optimal mode for intra prediction; an intra prediction means for performing intra prediction onto video inputted based on the mode selected in the mode selection means; a DCT and quantization means for performing DCT and quantization onto residual coefficients of a block outputted from the intra prediction means; and an entropy encoding means for performing entropy encoding onto DCT coefficients acquired from the DCT and quantization by using a scanning mode decided based on pixel similarity of the residual coefficients.

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: An encoder and an encoding method for a multi-channel signal, and a decoder and a decoding method for a multi-channel signal are disclosed. A multi-channel signal may be efficiently processed by consecutive downmixing or upmixing.

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: An encoder and an encoding method for a multi-channel signal, and a decoder and a decoding method for a multi-channel signal are disclosed. A multi-channel signal may be efficiently processed by consecutive downmixing or upmixing.

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.