18330467. METHOD FOR LEARNING MAPPING simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)

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METHOD FOR LEARNING MAPPING

Organization Name

TOYOTA JIDOSHA KABUSHIKI KAISHA

Inventor(s)

Shinichi Takeuchi of Nisshin-shi (JP)

Atsushi Tabata of Okazaki-shi (JP)

Shingo Noritake of Owariasahi-shi (JP)

Akira Murakami of Kasugai-shi (JP)

Ryo Yanagawa of Nagoya-shi (JP)

METHOD FOR LEARNING MAPPING - A simplified explanation of the abstract

This abstract first appeared for US patent application 18330467 titled 'METHOD FOR LEARNING MAPPING

Simplified Explanation

Abstract

A mapping learning model is described in this patent application, which uses a sound signal as input and outputs the cause of a sound in a vehicle. The learning method includes a signal correction process that corrects the sound signal by adding noise, and an update process that uses machine learning to update the mapping based on the corrected sound signal and its corresponding cause.

Bullet Points

  • The patent application describes a learning model that maps sound signals to their causes in a vehicle.
  • The learning method includes a signal correction process that adds noise to the sound signal to improve accuracy.
  • The mapping is updated through machine learning using the corrected sound signals as training data.
  • The cause of the sound is paired with the corrected sound signal as teaching data for the machine learning process.

Potential Applications

  • Automotive industry: This technology can be used in vehicles to identify the cause of various sounds, helping with diagnostics and maintenance.
  • Noise pollution monitoring: The mapping model can be applied to monitor and identify specific sounds in urban environments, helping to address noise pollution issues.
  • Product development: The technology can be used in the development of new vehicles or products to analyze and understand the causes of different sounds.

Problems Solved

  • Identification of sound causes: The technology solves the problem of accurately identifying the cause of a sound in a vehicle or other environments.
  • Noise interference: The signal correction process helps to minimize noise interference in the sound signal, improving the accuracy of the mapping model.

Benefits

  • Improved diagnostics: The technology enables accurate identification of sound causes, aiding in the diagnosis and troubleshooting of vehicle issues.
  • Efficient maintenance: By quickly identifying the cause of a sound, maintenance and repairs can be performed more efficiently, reducing downtime.
  • Noise pollution management: The mapping model can contribute to better management of noise pollution by identifying and addressing specific sources of noise.


Original Abstract Submitted

A mapping, which is as a subject of learning, is a learning model that uses a sound signal as an input variable and outputs a variable indicating the cause of a sound in a vehicle. A learning method includes a signal correction process that corrects a sound signal by superimposing a noise signal on the sound signal, and an update process that updates the mapping through machine learning in which the sound signal corrected in the signal correction process serves as training data and a cause of a sound paired with the sound signal serves as teaching data.

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