Active Learning - a technique employed to minimize manual annotation - the scheme reserves manual annotation for ambiguous/informative data points Power-Aware Feature Selection - conventional feature selection paradigm is extended such that the selection process also takes into account the computing cost of a candidate feature

1. Active Learning for Acoustic
Classification AND
Power Aware Feature Selection for
Audio/Sound Scene
Mulu W. Adhana
/Groep T Campus

2. Outline
2
• PhD Topic: Active Learning for Acoustic Scene/Event
Classification
o Active Learning: Introduction, principles, …
o Experimental Results
o Current Activities: adaptive, transfer learning, …
• Power Aware Feature Selection
o Proposed Method
o Experiments
o Conclusions

3. Active Learning: Introduction
3
• Traditional machine learning Techniques (Supervised)
o Abundant labeled data
o Labeling: labour intensive costly and error prone
o Domain expert
• Semi-supervised learning
• Active learning
o Seed information (very small in size)
o Sample unlabeled data points for annotation
o Involves user to actively annotate data point
• Reserved for informative/ambiguous ones

4. Active Learning Scheme
4

5. Active Learning in Practice
5
?
automatically
assigned
?
Small distance to separating
hyperplane
Ask the user

6. Active Learning: Sampling for annotation
6
• How ambiguous?
o Distance from separating line
o Probability assigned to the data point
o Entropy of the probability assigned to the data point

7. Active Learning: KLR
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• KLR: alternative to SVM with probabilistic outcome
o Natural extension to multi-class problem
o Probabilistic outcome (𝑝𝑖 = 𝒀 = 𝑦𝑖 𝑿 = 𝑥 ,
𝑖 = 1, … , 𝐶)
o Put threshold on some uncertainty measure: e.g.
𝑝 𝐶 − 𝑝 𝐶−1 > 𝑇 [2]

8. Results: KLR Active Learning
8
NAR dataset:
• 21 classes
• 431 annotated
examples
• 10-fold cross-
validation
• Reduce 84%
manual annotation

9. Active, Transfer and Incremental Learning
9
Annotated dataset
from different domain
Small Annotated
dataset
Source-task
• From different env’t
• With different
sensing devices
Knowledge
Target-task
• Slightly different
from the source
task

10. Power Aware Feature Selection
10
• Wearable Sensor Networks draw energy from battery
o Sensing, processing raw data, packet size,
o bit-depth while acquiring sound scene/event, …
• On-node processing. e.g. movement monitoring
o Extracting expensive features => Energy depletion
• Select inexpensive and information carrying features

11. Proposed Approach
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• Apply two criteria: Error rate and cost of extracting a
feature/subset of features(CPU-time)
• Incorporating dependency of feature extraction process
k<<n

12. Proposed Approach…
12
• Sequential forward selection, wrapper approach:
o Let 𝑌 holds already selected features, then
𝑆 = 𝑆 ∪ min
𝑋 𝑖
(𝑒𝑟𝑟𝑜𝑟(𝑆 ∪ 𝑋𝑖) + 𝜆 ∗ 𝐶𝑜𝑠𝑡(𝑆 ∪ 𝑋𝑖)/(|𝑆 ∪ 𝑋𝑖|)
where 𝜆 tradeoff ,
𝑆 ∪ 𝑋𝑖 −feature cardinality
𝑋𝑖 − random candidate feature
o Stopping criterion: add feature 𝑋𝑖 to Y such that
𝐶 𝑌 ∪ 𝑋𝑖 > 𝐶 𝑌

13. Feature Extraction: Dependency Graph
13

14. Experimental Setup
14
• Feature selection on the NAR dataset recorded by
humanoid robot NAO
Classes Audio Features
• 21 classes (door open,
door close, fridge open
close, moving chair, …)
• 431 examples (Sound
Scene)
35 (14-MFCC, 12-GTCC,
pitch Cross-correlation,
Spectral Rolloff, Spectral
Flatness, Spectral Flux,
Spectral Kurtosis , Spectral
Skewness , Spectral Slope,
Spectral Spread and ZCR)

15. Results
15

16. Questions