Training method and device for audio separation network, audio separation method and device, and medium

An apparatus for training an audio separation network, the apparatus comprising:

• at least one memory configured to store computer program code;
• and
• at least one processor configured to access said computer program code and operate as instructed by said computer program code, said computer program code comprising:
• first inputting code configured to cause at least one of the at least one processor to input mixed data of unlabeled audio into a teacher network, to obtain a first predicted interference signal and a first predicted clean signal;
• obtaining code configured to cause at least one of the at least one processor to obtain a first mixed signal by performing interpolation on the first predicted interference signal and the first predicted clean signal based on preset interference data;
• second inputting code configured to cause at least one of the at least one processor to input the first mixed signal into a student network that is untrained;
• training code configured to cause at least one of the at least one processor to train the student network to output a second predicted interference signal and a second predicted clean signal respectively corresponding to the first predicted interference signals and the first predicted clean signal;
• and
• adjusting code configured to cause at least one of the at least one processor to adjust the second predicted interference signal and the second predicted clean signal, such that a loss of a separation result meets a convergence condition.

A non-transitory computer-readable storage medium, storing computer code which, when executed by at least one processor, causes the at least one processor to at least:

• input mixed data of unlabeled audio into a teacher network, to obtain a first predicted interference signal and a first predicted clean signal;
• obtain a first mixed signal by performing interpolation on the first predicted interference signal and the first predicted clean signal based on preset interference data;
• input the first mixed signal into a student network that is untrained;
• train the student network to output a second predicted interference signal and a second predicted clean signal respectively corresponding to the first predicted interference signals and the first predicted clean signal;
• and
• adjust the second predicted interference signal and the second predicted clean signal, such that a loss of a separation result meets a convergence condition.