View source for 20240020525. SYSTEMS AND METHODS FOR AUTOMATIC ALIGNMENT BETWEEN AUDIO RECORDINGS AND LABELS EXTRACTED FROM A MULTITUDE OF ASYNCHRONOUS SENSORS IN URBAN SETTINGS simplified abstract (Robert Bosch GmbH)

=SYSTEMS AND METHODS FOR AUTOMATIC ALIGNMENT BETWEEN AUDIO RECORDINGS AND LABELS EXTRACTED FROM A MULTITUDE OF ASYNCHRONOUS SENSORS IN URBAN SETTINGS=


==Organization Name==

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


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Luca Bondi of Pittsburgh PA (US)|Luca Bondi of Pittsburgh PA (US)]][[Category:Luca Bondi of Pittsburgh PA (US)]]

[[:Category:Shabnam Ghaffarzadegan of Livermore CA (US)|Shabnam Ghaffarzadegan of Livermore CA (US)]][[Category:Shabnam Ghaffarzadegan of Livermore CA (US)]]

[[:Category:Samarjit Das of Wexford PA (US)|Samarjit Das of Wexford PA (US)]][[Category:Samarjit Das of Wexford PA (US)]]

==SYSTEMS AND METHODS FOR AUTOMATIC ALIGNMENT BETWEEN AUDIO RECORDINGS AND LABELS EXTRACTED FROM A MULTITUDE OF ASYNCHRONOUS SENSORS IN URBAN SETTINGS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240020525 titled 'SYSTEMS AND METHODS FOR AUTOMATIC ALIGNMENT BETWEEN AUDIO RECORDINGS AND LABELS EXTRACTED FROM A MULTITUDE OF ASYNCHRONOUS SENSORS IN URBAN SETTINGS

==Simplified Explanation==

The patent application describes a method for synchronizing audio stream data and sensor data in a data capture environment, and using this synchronized data to generate training data for a machine learning model.

* The method involves receiving audio stream data and sensor data associated with a data capture environment.
* Events in the sensor data are identified, and offset values are calculated for corresponding portions of the audio stream data.
* The sensor data is synchronized with the audio stream data, specifically for the portions corresponding to the identified events.
* The portion of the audio stream data corresponding to the events is labeled.
* Training data is generated using the labeled portion of the audio stream data.
* A machine learning model is trained using the generated training data.

Potential Applications:
* Speech recognition systems: The synchronized data can be used to train machine learning models for improved speech recognition in various environments.
* Environmental monitoring: The method can be applied to synchronize audio data and sensor data from environmental monitoring devices, enabling better analysis and understanding of the captured data.

Problems Solved:
* Synchronization of audio stream data and sensor data: The method solves the problem of accurately aligning audio and sensor data in a data capture environment, allowing for more precise analysis and training of machine learning models.

Benefits:
* Improved training data quality: By synchronizing the audio and sensor data, the method ensures that the training data used for machine learning models is accurately labeled and aligned, leading to improved model performance.
* Enhanced data analysis: The synchronized data enables more comprehensive analysis of the captured information, allowing for better insights and decision-making in various applications.


==Original Abstract Submitted==

a method includes receiving audio stream data associated with a data capture environment, and receiving sensor data associated with the data capture environment. the method also includes identifying at least some events in the sensor data, and calculating at least one offset value for at least a portion of the audio stream data that corresponds to at least one event of the sensor data. the method also includes synchronizing at least a portion of the sensor data associated with the portion of the audio stream data that corresponds to the at least one event of the sensor data, and labeling at least the portion of the audio stream data that corresponds to the at least one event of the sensor data. the method also includes generating training data using at least some of the labeled portion of the audio stream data, and training a machine learning model using the training data.



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