Natick, MA, United States of America

Karen Payton


Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:

goldMedal2 out of 10 
 
Speech Technology & Applied Research Corporation
 patents
silverMedal1 out of 832,912 
Other
 patents
where one patent can have more than one assignee

Years Active: 2019-2020

Loading Chart...
2 patents (USPTO):Explore Patents

Title: Karen Payton: Innovator in Signal Source Separation

Introduction

Karen Payton is a distinguished inventor based in Natick, MA (US). She has made significant contributions to the field of signal processing, particularly in environments characterized by multiple simultaneous sources. With a total of 2 patents, her work focuses on improving the separation of source signals from complex mixtures.

Latest Patents

Karen's latest patents include innovative methods for hypothesis-based estimation of source signals from mixtures. In environments such as acoustic and bioelectrical settings, effective blind source separation becomes challenging as the number of sources increases. Her inventions provide an adaptive filtering architecture that validates source hypotheses and extracts estimated representations of source signals. This approach enhances the separation of remaining hidden source signals from sensor response mixtures.

Career Highlights

Throughout her career, Karen has worked with notable organizations, including Speech Technology & Applied Research Corporation. Her expertise in signal processing has positioned her as a key player in advancing technologies that address complex source separation challenges.

Collaborations

Karen has collaborated with esteemed colleagues, including Richard Scott Goldhor and Keith Gilbert. These partnerships have contributed to her innovative work and the development of her patents.

Conclusion

Karen Payton's contributions to signal processing and source separation highlight her role as a leading inventor in her field. Her innovative patents continue to influence advancements in technology and improve the understanding of complex signal environments.

This text is generated by artificial intelligence and may not be accurate.
Please report any incorrect information to support@idiyas.com
Loading…