Audio signal compression method and apparatus using deep neural network-based multilayer structure and training method thereof

An audio signal compression apparatus comprising:

• a memory storing at least one instruction;
• and
• a processor executing the at least one instruction stored in the memory,
• wherein the at least one instruction enables the compression apparatus to perform (a) restoring, in a highest layer, an input audio signal as a first signal, (b) restoring, in at least one intermediate layer, a signal obtained by subtracting an upsampled signal, which is obtained by upsampling the audio signal restored in the highest layer or an immediately previous intermediate layer, from the input audio signal as a second signal, and (c) restoring, in a lowest layer, a signal obtained by subtracting an upsampled signal, which is obtained by upsampling the audio signal restored in an intermediate layer immediately before the lowest layer, from the input audio signal as a third signal,
• the first signal, the second signal, and the third signal being combined to output a final restoration audio signal and the highest layer, the at least one intermediate layer, and the lowest layer each comprising an encoder, a quantizer, and a decoder.

A method being executed by a processor for training a neural network compressing an audio signal in multiple layers, the method comprising:

• (a) compressing and restoring, in each layer, an input signal;
• and
• (b) comparing and determining a signal restored in each layer and a guide signal of the corresponding layer,
• wherein the signal input, at step (a), to each of the layers remaining after excluding a highest layer is a signal obtained by removing an upsampled signal, which is obtained by upsampling a signal obtained by combining the signal restored in a previous layer and a guide signal of the previous layer at a predetermined ratio, from the input audio signal.