Encoding method, decoding method, encoder for performing encoding method, and decoder for performing decoding method

A decoding method comprising:

• outputting quantized LP coefficients, a first quantized latent signal, a second quantized latent signal, and a quantized weight vector by de-quantizing LP coefficients bitstream, a first bitstream, a second bitstream, and a weight bitstream, respectively using a de-quantization module;
• outputting a first decoded residual signal obtained by decoding the first quantized latent signal, using a fourth neural network:
• outputting a second decoded residual signal obtained by decoding the second quantized latent signal, using a second neural network module, using a fifth neural network;
• reconstructing a residual signal using the first decoded residual signal, the second decoded residual signal, and the quantized weight vector;
• and
• synthesizing an output signal by feeding the reconstructed residual signal into an LP synthesis filter constructed by the quantized LP coefficients,
• wherein the fourth neural network comprises a recurrent neural network (RNN) configured to decode a periodic component of the residual signal,
• wherein the fifth neural network comprises a feedforward neural network (FNN) configured to decode a non-periodic component of the residual signal.

An encoder comprising:

• a processor,
• wherein the processor is configured to:
• output LP coefficients bitstream and a residual signal by performing an LP analysis on an input signal, using an LP analysis module;
• output a first latent signal obtained by encoding a periodic component of the residual signal, using a first neural network;
• output a second latent signal obtained by encoding a non-periodic component of the residual signal, using a second neural network,
• output a weight vector for each of the first latent signal and the second latent signal, using a first neural network module;
• and
• output a first bitstream obtained by quantizing the first latent signal, a second bitstream obtained by quantizing the second latent signal, and a weight bitstream obtained by quantizing the weight vector, using a quantization module
• wherein the first neural network comprises a recurrent neural network (RNN) configured to encode a periodic component of the residual signal,
• wherein the second neural network comprises a feedforward neural network (FNN) configured to encode a non-periodic component of the residual signal.