View source for 17957581. SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA simplified abstract (Robert Bosch GmbH)

=SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA=


==Organization Name==

[[:Category:Robert Bosch GmbH|Robert Bosch GmbH]]


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Ivan Batalov of Pittsburgh PA (US)|Ivan Batalov of Pittsburgh PA (US)]][[Category:Ivan Batalov of Pittsburgh PA (US)]]

[[:Category:Thomas Alber of Filderstadt (DE)|Thomas Alber of Filderstadt (DE)]][[Category:Thomas Alber of Filderstadt (DE)]]

[[:Category:Filipe J. Cabrita Condessa of Pittsburgh PA (US)|Filipe J. Cabrita Condessa of Pittsburgh PA (US)]][[Category:Filipe J. Cabrita Condessa of Pittsburgh PA (US)]]

[[:Category:Florian Lang of Karlsruhe (DE)|Florian Lang of Karlsruhe (DE)]][[Category:Florian Lang of Karlsruhe (DE)]]

[[:Category:Felix Schorn of Renningen (DE)|Felix Schorn of Renningen (DE)]][[Category:Felix Schorn of Renningen (DE)]]

[[:Category:Carine Au of Stuttgart (DE)|Carine Au of Stuttgart (DE)]][[Category:Carine Au of Stuttgart (DE)]]

[[:Category:Matthias Huber of Saint-Petersburg (RU)|Matthias Huber of Saint-Petersburg (RU)]][[Category:Matthias Huber of Saint-Petersburg (RU)]]

[[:Category:Dmitry Naumkin of Saint-Petersburg (RU)|Dmitry Naumkin of Saint-Petersburg (RU)]][[Category:Dmitry Naumkin of Saint-Petersburg (RU)]]

[[:Category:Michael Kuka of Waiblingen (DE)|Michael Kuka of Waiblingen (DE)]][[Category:Michael Kuka of Waiblingen (DE)]]

[[:Category:Balázs Lipcsik of Kistokaj (HU)|Balázs Lipcsik of Kistokaj (HU)]][[Category:Balázs Lipcsik of Kistokaj (HU)]]

[[:Category:Martin Boschert of Stuttgart (DE)|Martin Boschert of Stuttgart (DE)]][[Category:Martin Boschert of Stuttgart (DE)]]

[[:Category:Andreas Henke of Diemelstadt (DE)|Andreas Henke of Diemelstadt (DE)]][[Category:Andreas Henke of Diemelstadt (DE)]]

==SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17957581 titled 'SYSTEM AND METHOD FOR DEEP LEARNING-BASED SOUND PREDICTION USING ACCELEROMETER DATA

==Simplified Explanation==

The system described in the abstract utilizes vibrational and sound information from sensors to predict the sound emitted by a device in real-time using a machine learning model.

* The system includes a processor communicating with sensors to collect vibrational and sound information from a test device.
* The processor generates a training data set using the collected data, which is then used by a machine learning model to output sound predictions.
* Real-time vibrational data from a run-time device is received, and a sound prediction is generated based on the machine learning model and the real-time data.

== Potential Applications ==
This technology could be applied in various industries such as manufacturing, automotive, and consumer electronics for predictive maintenance, quality control, and performance optimization.

== Problems Solved ==
This technology helps in predicting potential issues or malfunctions in devices based on their vibrational and sound patterns, allowing for proactive maintenance and reducing downtime.

== Benefits ==
The system enables early detection of anomalies in devices, leading to cost savings, increased efficiency, and improved overall performance.

== Potential Commercial Applications ==
* Predictive maintenance solutions for industrial equipment
* Quality control systems for consumer electronics manufacturing
* Performance optimization tools for automotive companies

== Possible Prior Art ==
One possible prior art could be the use of machine learning models in predictive maintenance systems for industrial machinery.

=== What is the accuracy rate of the sound predictions generated by the machine learning model? ===
The accuracy rate of the sound predictions would depend on the quality and quantity of the training data set used to train the machine learning model, as well as the complexity of the sound patterns emitted by the devices.

=== How does the system handle different types of devices with varying sound profiles? ===
The system likely utilizes a flexible machine learning model that can adapt to different sound profiles by continuously learning and updating its predictions based on the real-time data received from the devices.


==Original Abstract Submitted==

A system includes a processor in communication with one or more sensors. The processor is programmed to receiving, from the one or more sensors, vibrational information and sound information associated with the vibrational information from a test device, generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions, receiving real-time vibrational data from a run-time device running an actuator or electric dive emitting the real-time vibrational data, and based on the machine learning model and the real-time vibrational data, output a sound prediction indicating a purported sound emitted from the run-time device.



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