RESEARCH
Acoustic & Speech
Investigates various Speech signal processing schemes for acoustic modeling so that more robust speech recognition can be achieved. Our aim is to perform the state-of-art research providing effective means for achieving:
Context Awareness System based on Acoustic & Visual
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날짜 2021-04-08 21:49:18
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Context Awareness System based on Acoustic & Visual
Contents
2. Main algorithm and principle
This is the context awareness system based on acoustic and visual data in mobile platform. By analyzing the acoustic and visual source from the microphone and camera of the smart-phone (mobile device), we can get some cues for context. The Environments we can recognize are followings:
- 10 Acoustic environments
|
Environment |
Description |
|
Babble |
many people are saying in crowded places(cafeteria or coffee shop) |
|
Car |
bus, automobile, street(roadside) |
|
Moving |
moving in closed space (pocket, bag) |
|
Music |
pop, classic, rock |
|
Noisy |
high level audio |
|
Office |
printing, typing keyboard, phone ringtone |
|
One speaker talking |
only one person is saying(presentation, lecture) |
|
Silent |
no talking, silence |
|
Subway |
platform and inside of subway |
|
Water |
washstand, sink, shower room |
- 4 visual environments
|
Low intensity |
Face |
|
|
|
|
Indoor |
Outdoor |
|
|
|
|
Moving Detection |
|
|
|
|
2. Main algorithm and principle [Go Top]
- Acoustic
1. Noisy environments detection using the average value of log sound energy
2. MFCC feature extraction for context awareness
3. GMM(Gaussian Mixture Model) for the classification
- Visual
1. Low intensity : using the image intensity, light detector
2. Face: skin region detector
3. Indoor/Outdoor : Nearest neighbor classifier using color/texture features
4. Moving detection : Neural network classifier using the optical flow for feature
- high speed in mobile platform for real-time
1. Dynamic mixture GMM for acoustic modeling
2. Recognition process scheduling
- System Architecture
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