Visualization Experiments
From SpeechWiki
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.
Contents |
Dramatis personae
Mark Hasegawa-Johnson Camille Goudeseune Grads: Sarah Borys, Lae-Hoon Kim, Zhen Li, Kai-Hsiang Lin, 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
Zhen, Kai-Hsiang: recruit analyst-subjects, schedule experiments
Zhen, Kai-Hsiang: September, run 5-subject experiment.
Camille or Mark: 2010 Dec 9-10, present at FODAVA annual review, Georgia Tech.
Notes
How combine features?
Feature generators read a recorded sound and write a feature file.
Camille runs Sarah's script feat.pl, to read a 16 kHz amicorpus .wav and write .fb and .mfcc files. 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.