Abstract:
Provided are a signal processing method for determining an audience rating of media, and an additional information inserting apparatus, a media reproducing apparatus and an audience rating determining apparatus for performing the same method. In detail, the signal processing method for determining an audience rating of media is a method that may determine an audience rating of media with respect to a whole section of an audio signal by inserting additional information into a silence section through a noise signal.
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 audio encoding apparatus to encode an audio signal using lossless coding or lossy coding and an audio decoding apparatus to decode an encoded audio signal are disclosed. An audio encoding apparatus according to an exemplary embodiment may include an input signal type determination unit to determine a type of an input signal based on characteristics of the input signal, a residual signal generation unit to generate a residual signal based on an output signal from the input signal type determination unit, and a coding unit to perform lossless coding or lossy coding using the residual signal.
Abstract:
An apparatus and a method for correcting colors of an image projection device are provided. The method includes: acquiring a photographed image by photographing a sample image projected on projection surface; generating input-output color information for n regions, based on color values of a block in the sample image and corresponding color values of the block in the photographed image; selecting one of the n regions of photographed images as a reference region; generating look-up tables (LUTs) for non-reference regions, based on the reference region and the input and output color information; and correcting colors of input images to be projected by the image projection device using the look-up tables, thereby minimizing color difference of the input images on the projection surface for both intra and inter projection device color correction while simplifying the correction procedure.
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:
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 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 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:
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.