LEARNING APPARATUS, LEARNING METHOD AND PROGRAM
One aspect of the present invention is a learning device including a self-learning unit that updates content of main conversion processing for converting data to be processed into data in a predetermined format by executing self-supervised learning, and a data augmentation unit that executes data augmentation processing of generating data to be processed in the main conversion processing based on an acoustic time series, in which the data augmentation unit performs acoustic time series clipping processing of clipping a partial time series that is a time series of a part of the acoustic time series, duplication processing of duplicating the partial time series, and conversion processing of converting one and the other of the partial time series according to a predetermined rule, and the self-learning unit updates the content of the main conversion processing by self-supervised learning based on a result obtained by the conversion processing.
1 . A learning device comprising:
a processor; and
a storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by the processor, perform processing of:
updating content of main conversion processing for converting data to be processed into data in a predetermined format by executing self-supervised learning; and
executing data augmentation processing of generating data to be processed in the main conversion processing based on an acoustic time series, wherein
acoustic time series clipping processing of clipping a partial time series that is a time series of a part of the acoustic time series, duplication processing of duplicating the partial time series, and conversion processing of converting one and the other of the partial time series according to a predetermined rule are performed in the data extension processing, and
the content of the main conversion processing is updated by self-supervised learning based on a result obtained by the conversion processing.
2 . The learning device according to claim 1 , wherein
the conversion processing includes first mix-up processing of changing a first partial time series that is one of partial time series using a first mixed time series that is another time series, and second mix-up processing of changing a second partial time series that is the other of partial time series using a second mixed time series different from the first mixed time series.
3 . The learning device according to claim 2 , wherein
using information indicating an intensity for each set of frequency and time as acoustic image data, the conversion processing includes first random resizing processing of executing affine conversion on at least a part of acoustic images expressing a first mixing time series that is a first partial time series after change by the first mix-up processing, and second random resizing processing of executing affine conversion on at least a part of acoustic images expressing a second mixing time series that is a second partial time series after change by the second mix-up processing.
4 . The learning device according to claim 1 , wherein
the conversion processing includes first random resizing processing of executing affine conversion on at least a part of an acoustic image representing a first partial time series that is one of partial time series, and second random resizing processing of executing affine conversion on at least a part of an acoustic image representing a second partial time series that is the other of partial time series.
5 . A learning method comprising:
updating content of main conversion processing for converting data to be processed into data in a predetermined format by executing self-supervised learning; and
executing data augmentation processing of generating data to be processed in the main conversion processing based on an acoustic time series, wherein
in the data augmentation processing, acoustic time series clipping processing of clipping a partial time series that is a time series of a part of the acoustic time series, duplication processing of duplicating the partial time series, and conversion processing of converting one and the other of the partial time series according to a predetermined rule are performed, and
the content of the main conversion processing by self-supervised learning based on a result obtained by the conversion processing is updated.
6 . A non-transitory computer readable medium which stores a program for causing a computer to function as the learning device according to claim 1 .