Yamaha has filed a patent for an audio device simulation method. The method involves acquiring a standard model that represents the input-output characteristics of an audio device. It also includes setting parameters of a target audio device and measuring its input-output characteristics. The patent aims to generate an individual model for the target audio device by correcting the standard model using the measured input-output characteristics. GlobalData’s report on Yamaha gives a 360-degree view of the company including its patenting strategy. Buy the report here.

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According to GlobalData’s company profile on Yamaha, AR audio was a key innovation area identified from patents. Yamaha's grant share as of September 2023 was 37%. Grant share is based on the ratio of number of grants to total number of patents.

Audio device simulation method using standard and individual models

Source: United States Patent and Trademark Office (USPTO). Credit: Yamaha Corp

A recently filed patent (Publication Number: US20230317043A1) describes an audio device simulation method and system. The method involves acquiring a standard model that represents the input-output characteristics of an audio device. Then, the method sets at least one parameter of a target audio device of the same type and measures its input-output characteristics. Using the measured data, an individual model is generated by correcting the standard model.

In claim 2, it is mentioned that multiple parameters can be set, and the measuring process is performed for each of these parameters. This allows for a more comprehensive understanding of the target audio device's characteristics.

Claim 3 introduces the use of deep learning to generate the individual model. The measured input-output characteristics are used as training data for the deep learning process. This indicates that the method leverages advanced machine learning techniques to improve the accuracy of the individual model.

Claim 4 expands on the deep learning aspect by stating that the standard model is initially generated through a first deep learning process. The individual model is then created by correcting the standard model using a second deep learning process. This two-step deep learning approach further enhances the accuracy and customization of the individual model.

The measuring process described in claim 5 involves using a music signal, while claim 6 mentions the use of a measurement signal. These signals are utilized to measure the input-output characteristics of the target audio device. The choice of signal may depend on the specific requirements of the simulation.

Claim 7 introduces the use of a motor to move an operator of the target audio device. This operator is designed to receive changes to the parameters being set. The motor allows for automated adjustments to the target audio device, facilitating the measurement process.

The patent also describes an audio device simulator and an audio device simulation system. The simulator includes a processor that acquires the standard model and measures the input-output characteristics of the target audio device. The individual model is then generated by correcting the standard model using the measured data.

Overall, this patent presents a method and system for simulating audio devices. By acquiring a standard model and measuring the input-output characteristics of a target audio device, an individual model can be generated using deep learning techniques. This individual model provides a more accurate representation of the target audio device's characteristics. The use of different signals and a motorized operator further enhances the measurement process.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.