Methods of encoding and decoding audio signal, and encoder and decoder for performing the methods
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.
1. A method of encoding an audio signal, the method comprising:
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,
wherein the first encoding model is a neural network model trained based on a time domain difference between an output signal generated from the final latent vector and the audio signal and a frequency domain difference between the output signal and the audio signal.
2. The method of claim 1 , wherein the first encoding model comprises a plurality of scale blocks for down-sampling and a convolution layer for performing a convolution operation.
3. A method of decoding an audio signal, the method comprising:
identifying a bitstream generated by an encoder;
extracting a latent vector from the bitstream; and
generating an output signal by inputting the latent vector to a decoding model,
wherein the latent vector is a combination of a latent vector indicating a time domain feature of an input signal and a latent vector indicating a frequency domain feature of the input signal, and
the input signal is an audio signal included in a low frequency band of the audio signal,
wherein the decoding model is a neural network model trained based on a time domain difference between the output signal and the audio signal and a frequency domain difference between the output signal and the audio signal.
4. A method of training neural network models used to encode and decode an audio signal, the method comprising:
windowing an input signal corresponding to a low frequency band of the audio 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;
generating an output signal by inputting the final latent vector to a decoding model; and
training the first encoding model and the decoding model based on a time domain difference between the output signal and the audio signal and a frequency domain difference between the output signal and the audio signal.
5. The method of claim 4 , wherein the first encoding model comprises a plurality of scale blocks for down-sampling and a convolution layer for performing a convolution operation.