The Chicago section of the Audio Engineering Society is having an open meeting that might be of interest to some of you. The presentation topic is about using deep learning to recognize sound sources. (!)
Feel free to get in touch with me, either directly or by responding here if you have any questions about the event, want to know more about the AES, or would like to put a group together to go out to the Shure building.
I’ve pasted the entire meeting notice below:
Chicago AES Section - Meeting Notice (February 23, 2017)
Please forward this notice to interested friends and colleagues. Members and nonmembers are welcome.
The next meeting of the Chicago Section of the Audio Engineering Society will be held at 7:30pm on Thursday, February 23, 2017.
Find us online at: www.aes.org/sections/chicago/
Join our LinkedIn group at: www.linkedin.com/grp/home?gid=2427470
TOPIC:
Recognizing and Separating Sounds: Deep Learning in Real-World Audio Signal Processing
PRESENTER:
John Woodruff
Knowles Corporation
DATE:
Thursday, February 23, 2017
TIME:
7:30 pm (Optional dinner at 6:30 pm)
LOCATION:
Shure Incorporated, 5800 W. Touhy Ave, Niles, IL 60714
Enter at the employee entrance on the east side of the building and register at the guard desk. A valid driver’s license (or government-issued photo ID) must be presented at the guard desk when registering.
DIRECTIONS:
Driving: When arriving by car, approach from the east by heading west on Touhy, then turn right into the parking lot just east of the Shure building, which is on the corner of Touhy and Lehigh. DO NOT turn left into parking lot from Touhy heading east, as this is illegal and you may get a traffic ticket.
CTA Blue Line: Get off at Jefferson Park transit center, and take bus lines 85A, 225, or 226 to Shure (be sure to check the bus schedules for return trip).
CTA Red Line: Get off at Howard and take bus line 290 to Shure (be sure to check the bus schedules for return trip).
DINNER:
Dinner (optional, but please RSVP) will begin at 6:30pm.
Contact Giles Davis (gilesdavis@motorola.com) by Wednesday, February 22nd if you would like to join us.
Pizza and salad from Lou Malnati’s will be provided. Please let Giles know if you have a preference for vegetarian, gluten-free, etc.
Price is $10 for non-members and $8 for members and students (please bring cash).
DESCRIPTION:
Listeners with normal hearing can recognize the source of a sound, localize the point of origin, and separate the information provided by an individual source from competing sound sources. There is a longstanding interest in developing algorithms to achieve these capabilities in commercial products. Performance for some problems, such as automatic speech recognition (ASR), has improved substantially in recent years. Conventional signal processing techniques, however, are still widely deployed for the problem of sound separation in spite of well-known limitations.
Supervised learning algorithms have been central to the advances achieved in ASR, and such algorithms are poised to displace or augment long-standing signal processing methods used for sound separation. Recent literature has shown that new approaches to sound separation enabled by machine learning may lead to transformative differences in user experience. One example is improving speech intelligibility in a noisy environment for a hearing aid user. Many technical challenges remain to be overcome before we see widespread deployment of these methods.
In this discussion we will cover the basic concepts and acoustic cues involved in conventional approaches to sound separation, such as beamforming and speech enhancement. We will also introduce recent supervised learning approaches to sound separation and discuss where these can be used in combination with, or to replace conventional methods. Finally, we will talk about the challenges in deploying supervised learning methods for sound separation in real-world products.
ABOUT THE PRESENTER:
John Woodruff leads audio processing algorithm development for Knowles’ Intelligent Audio division in Mountain View, CA. He has been with Knowles and Audience since 2012, developing algorithms for detection, localization, classification, separation and enhancement of audio signals, and helping to deploy those algorithms in smart phones, laptops and other consumer devices. Prior to joining Audience, he worked on algorithms for sound separation and localization, pitch tracking, and music remixing in the Perception and Neurodynamics Lab at Ohio State University and the Interactive Audio Lab at Northwestern University. John received a Ph.D. in computer science and engineering from Ohio State University, a M.Music in music technology from Northwestern University, and a B.Sc. in mathematics from the University of Michigan.
MEMBERSHIP:
Not a member of the AES? For information about joining, go to www.aes.org/membership/.
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