Abstract:
Disclosed is an audio watermark insertion method. The audio watermark insertion method includes performing a modulated complex lapped transform (MCLT) on a first audio signal, inserting a bit string of a watermark in the first audio signal obtained by performing the MCLT, performing an inverse modified discrete cosine transform (IMDCT) on the first audio signal in which the bit string is inserted, and obtaining a second audio signal, which is the first audio signal in which the watermark is inserted, by performing an overlap-add on a signal obtained by performing the IMDCT and a neighbor frame signal.
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:
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:
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:
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 an apparatus and a method of controlling an eye-to-eye contact function, which provide a natural eye-to-eye contact by controlling an eye-to-eye contact function based on gaze information about a local participant and position information about a remote participant on a screen when providing the eye-to-eye contact function by using an image combination method and the like in a teleconference system, thereby improving absorption to a teleconference.
Abstract:
Disclosed is an apparatus and method for audio encoding/decoding that is robust against coding distortion in a transition section. An audio encoding method includes outputting a frequency domain signal by time-to-frequency (T/F) transform of an input signal, outputting a frequency domain residual signal in which a frequency axis envelope is removed from the frequency domain signal by applying frequency domain noise shaping (FDNS) encoding to the frequency domain signal, outputting a time domain residual signal in which a time axis envelope is removed by performing linear prediction coefficient (LPC) analysis based on the frequency domain residual signal, and quantizing and transmitting the time domain residual signal.
Abstract:
Provided is a digital twin disaster management system customized to keep safety for urban underground tunnels, including: a sensor sub-system configured to detect environmental information, status information and image information in the urban underground tunnels; a digital twin model management sub-system configured to create and update a virtual space corresponding to the urban underground tunnels using information provided from the sensor sub-system and 3D space, insert various types of attributes into the virtual space, detect tagging information, predict the spread of each disaster, and infer a degree of risk of a management facility; a disaster management sub-system having a control function of conducting centralized supervision by displaying information about components installed in the urban underground tunnels in the metaverse space and recording a situation; and a network sub-system configured to provide the virtual space to a user terminal of an external inspector.