Visualization Experiments

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Revision as of 21:16, 2 July 2010

Visualization experiments include at least the following components:

Visualization Interface Number 1 --- Timeline Audio ("timeliner")

Lightweight, for laptops or handhelds. Similar to, and compared against, audacity.

Visualization Interface Number 2 --- Milliphone

A command center interface, designed for the Cube.

 Qualifying round is a tutorial.  Expect 1 or 2 out of 6 recruited subjects to fail this.
 Baseline uses Audacity-style viz, i.e. peak amplitude + spectrogram.
 Fancier uses dsp viz (no neural net).
 Fanciest uses neural net.

Feature Computation --- Signal Features

These features rapidly give an analyst information about the signal, e.g., spectrograms.

Feature Computation --- Classification Features

These features measure how well a given classification label is matched by the signal at a given point in time (confidence score). Labels may be defined before or during a session.

Dramatis personae

 Mark Hasegawa-Johnson
 Camille Goudeseune
 Grads: Sarah Borys, Lae-Hoon Kim, Xi Zhou, Xiaodan Zhuang
 Undergrads: David Cohen

Tasks

Camille: keep developing timeliner

 done: pan, zoom (mouse scrollwheel)
 done: gui using ruby-opengl, for all OSes: heron ibex leopard xp vista
 done: inline C generate texturemaps
 later: scrollweel-glut workaround for windows

Camille: keep developing milliphone, hand off to gradstudents

All: run timeliner

 load a recorded sound
 load precomputed features to display
 select and play intervals

Grads: choose features, code feature generators

David: measure and model computation speed of feature generators

Camille: map features to HSV

Grads: design and pilot-study experiments

Grads: recruit analyst-subjects, schedule experiments

Grads: September, run 5-subject experiment. November, present at FODAVA meeting, Richland WA

Notes

How combine features?

Feature generators read a recorded sound and write a feature file.

 format: http://labrosa.ee.columbia.edu/doc/HTKBook21/node58.html#SECTION03271000000000000000
 http://htk.eng.cam.ac.uk/
 Sequence of feature vectors.

Later: stream not batch.

Experiment tasks:

 find instances of a class of sound events
 find anomalous sounds (open-ended, vague)

Recorded sounds

 AMI meeting room transcribed
 fieldrecorder/090216
 fieldrecorder aircraft + webcam for ground truth
   play freqsweep through genelec into fieldrecorder.
   ignore clock drift.
   Keep data files small enough for our tools.
 toy
   ruby script plus short audio source files generates a long target file.  Tweak script while tweaking apps.

Realtime server (later)

 record audio
 circular buffer, a few months long
 compute features at multiple scales
   fast approximate algorithms for caching of features.
 stream all this to a googlemaps-ish server
 when client scrolls (pans) or zooms, it requests fresh data from server

Logistics

Timeliner: in BI 2253 or 2nd floor printing room? PC with Ubuntu 8.1 and 4GB RAM.

Camille will provide headphones, mouse with scrollwheel, extra RAM, hard disk for ubuntu.