Learning data generation device, learning data generation method, and program

A training data generation method for use in a training data generation device, the method comprising:

• correcting training data that is time-series data that indicates states of an object and in which a label corresponding to a state of the object is assigned to each section that corresponds to the state indicated by the label,
• wherein in correcting the training data, a break time that divides a first section from a second section adjacent to the first section is corrected based on time-series data in the first section and time-series data in the second section,
• the break time is corrected so as to maximize a sum of a difference between an average of the time-series data in the first section and an average of the time-series data in the second section and a difference between a variance of the time-series data in the first section and a variance of the time-series data in the second section, and
• the training data, after correction, is used to train a model that estimates and outputs a condition of a road surface corresponding to the training data.

A non-transitory computer-readable medium encoded with computer-readable instructions that, when executed by a processor, cause the processor to perform a method comprising:

• correcting training data that is time-series data that indicates states of an object and in which a label corresponding to a state of the object is assigned to each section that corresponds to the state indicated by the label,
• wherein in correcting the training data, a break time that divides a first section from a second section adjacent to the first section is corrected based on time-series data in the first section and time-series data in the second section,
• the break time is corrected so as to maximize a sum of a difference between an average of the time-series data in the first section and an average of the time-series data in the second section and a difference between a variance of the time-series data in the first section and a variance of the time-series data in the second section, and
• the training data, after correction, is used to train a model that estimates and outputs a condition of a road surface corresponding to the training data.