Hearing Seminars
CCRMA hosts a weekly Hearing seminar. All areas related to perception are discussed, but the group emphasizes topics that will help us understand how the auditory system works. Speakers are drawn from the group and visitors to the Stanford area. Most attendees are graduate students, faculty, or local researchers interested in psychology, music, engineering, neurophysiology, and linguistics. Meetings are usually from 10:30AM to 12:20 (or so, depending on questions) on Friday mornings in the CCRMA Seminar Room.
The current schedule is announced via a mailing list. To be added to the mailing list, send email to hearing-seminar-request@ccrma.stanford.edu. If you have any questions, please contact Malcolm Slaney at hearing-seminar-admin@ccrma.stanford.edu.
Recent Hearing Seminars
Hotword Sifter - Make Google Home hear you in cocktail parties
Date:Fri, 03/16/2018 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarIt's the last unsolved problem in speech recognition. :-) How does one use minimal resources to recognize hotwords like "Hey Google" or "Alexa", in the face of noisy 24x7 environments. These keywords (or hotwords) are the signal to turn on full speech recognition on the new generation of always listening devices, and the hotword must be recognized accurately, and in multiple acoustic environments. Recognizing these keywords must be done on the device, as nobody wants all their speech transmitted to the cloud.
FREEOpen to the PublicHearing Sound with Auditory Hair Bundles
Date:Fri, 03/09/2018 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarHow is all that beautiful music converted from acoustic waves into spikes? The key biological component are some very sensitive cilia, hair-like structures, that sit at the end of auditory nerve cells. They move in response to passing waves, and that unleases spikes the head toward the auditory brain. It really is quite magical.
Who: Wisam Reid (Stanford)
What: Relating the Cohesiveness of Auditory Hair Bundles in Mammals to their Function
When: Friday March 9th at 10:30AM
Where: CCRMA Seminar Room (Top Floor of the Knoll)
Why: These are really quite amazing structures.
Bring your cilia to CCRMA and Wisam will talk about how they do their important job.
FREEOpen to the PublicYaqing Su on Neural Coding of Pitch Cues in the Auditory Midbrain
Date:Fri, 02/16/2018 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarNeural Coding of Pitch Cues in the Auditory MidbrainNeural Coding of Pitch Cues in the Auditory Midbrain music perception that pitch??!!?!? And just how do we perceive pitch? After many decades it’s still a source of many disagreements, and much research.
Pitch is critical to auditory perception. It’s an important cue for separating sounds in a cocktail party. It binds a speech signal together so you hear speech instead of a bunch of chirps. Yet, the exact mechanism that our brain uses to hear the pitch of a signal is not known. Yaqing Su will be talking about evidence she has found that suggests that pitch is perceived with a combination of measures, effectively sidestepping the argument about place vs. time. This will be fun.FREEOpen to the PublicDecoding EEG Signals by Malcolm Slaney
Date:Fri, 02/02/2018 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarThere are many ways to interpret EEG signals, but the newest approach eschew averaging and perform single-shot decoding. This allows for real-time information about how a subject is perceiving a sound. How does one connect arbitrary audio to the resulting EEG signals? Or how does one predict the audio that resulted in a particular EEG signal? Can I tell you the sound you are attending to? I'll be talking about recent work using linear and correlation methods to build better models that connect audition and EEG signals. This a form of system identification, and has been shown to work for decoding which of two audio signals one is attending to. Cool stuff.
Who: Malcolm Slaney (Google and CCRMA)
What: EEG Decoding Algorithms and ResultsFREEOpen to the PublicModeling fine time structure in the brain
Date:Fri, 01/19/2018 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarWe really don't know how auditory *information* is represented in the brain. The easiest approach relies on the rate profile, simply a measure of how fast a group of neurons is firing. But the rate profile ignores a lot of information in the exact timing of each spike. And at a higher level there are oscillatory representations. All of these representations can be used to encode information, but which is most important and leads to the best model of auditory perception?
FREEOpen to the PublicAudio Style Transformations using Deep Neural Networks by Prateek Verma
Date:Fri, 01/12/2018 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing Seminar
Deep Neural Networks (DNNs) have been wildly successful for many tasks, but none of the tasks are as wondrous as the success that DNNs have had on transferring the style of one painting to another painter’s art. This magical trick is accomplished by mixing and matching the low-level feature analysis layers between different styles of paining, so a painting in one style is rendered in another with different kinds of brush works.
But can we do this for audio? Prateek Verma has been experimenting with this, and will talk about his work and results. What does audio style mean, and how does one capture it?
Who: Prateek Verma (Stanford CCRMA)
What: Audio Style Transformations using Deep Neural NetworksFREEOpen to the PublicElisabetta Chicca on spike-based learning
Date:Thu, 12/07/2017 - 5:00pm - 5:50pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarSpike...Spike.....Spike..Spike........Spike. That's how the brain communicates. But how do you spikes help you learn? And more importantly, how do you modify a spiking network to learn new things (like speech and music)? The colloquial rule is "those who fire together, wire together" but in practice you need causality. A more realistic learning model is called spike-timing-dependent plasticity, where a particular neural connection is strengthened when an input spike immediately precedes the output spike. An input spike must happen before the output. This is the backprop, for those of you from the machine-learning or DNN world, of the real neural world.
FREEOpen to the PublicWeighing acoustic factors in music and language during development
Date:Fri, 12/01/2017 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarChristina M. Vanden Bosch der Nederlanden will be talking about the differences between speech and music when we develop our auditory brains. They seem pretty different to us now, but how do our young brains parse the cachophony of sounds and decide that some sounds are meant to be recognized as words, and other sounds are just to make us feel good. Or are they both?
Who: Christina M. Vanden Bosch der Nederlanden
What: Weighing acoustic factors in music and language during development
When: 10:30AM on Friday December 1, 2017
Where: CCRMA Seminar Room, top floor of the Knoll at Stanford
Why: How do we learn the meaning (or not) of sounds??Open to the PublicJan Skoglund on Objective Quality Assessment for Immersive Audio
Date:Fri, 11/10/2017 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarWith the surging interest in augmented and virtual reality, there is more and more interest in high-quality 3D sound rendering. We heard many talks about this last year, with very smart people rendering sounds in complicated physical environments. My favorite was one group who said they could model the sounds from a hallway the user couldn't see. But does this matter? I'm not sure I could tell the difference. But I'm mindful of the fact that at one point people thought LP records were the ultimate in audio fidelity.
FREEOpen to the PublicAren Jansen on AudioSet: Real world audio event classification
Date:Fri, 11/03/2017 - 10:30am - 12:00pmLocation:CCRMA Seminar RoomEvent Type:Hearing SeminarAudioSet is an attempt to do for audio processing what big image databases like ImageNet have done for computer vision. Arguably big image datasets like ImageNet, by Prof. Li Fei-Fei at Stanford, and the competitions they have spawned, have advanced image recognition more than any other research result.
Aren Jansen, from Google, will be talking about AudioSet. He will talk about their data collection effort, how the data is organized and the first results on sound object recognition from this large dataset. By large, they mean 2.1M human-annotated videos, 5.8M hours of audio, and 527 classes of sounds.FREEOpen to the Public
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