1. Audio Content Description
with
Wavelets
Neural Nets
and
Diploma Thesis Stephan Rein
Prof. Dr.-Ing. Thomas Sikora
Prof. Dr. Martin Reisslein
Dr. Nicolas Moreau

2. Overview Next Generation Internet Search Machine
MPEG-7: Multimedia Content Description
Why Wavelets?
Statistical Analysis of Wavelet Coefficients
Neural Nets for Audio Content Classification
Results
Summary

3. Next Generation Internet Search Machine
identifiy classical movements
So.1 iii
Men 57
feature
extraction
similarity
measure
So.1 iv
Men 57
So.1 iv

4. Moving Pictures Expert Group
MPEG-1, 2, 4: Compression of Multimedia Data
MPEG-7: Description of Multimedia Data
Idea of Multimedia content description: key to
completely novel and futuristic applications
Content description tools
Platform for Descriptive Data
Encourage research on content description

5. 6 Sonatas & Partitas for the
Test Data Base:
J. S. Bach
6 Sonatas & Partitas for the
Solo Violin
BWV
1957
1952
1973
Current today, current in 100 years from now
Recordings differ in time, frequency, quality and
sound environments
Polyphonic and non separable phenomena

6. Problem Short-Term Fourier Analysis:
Trade-off between Time and Frequency
(Heisenberg Uncertainty Principle)
Short analysis window: high frequencies can be well located, but
low frequency components can not be measured
Long analysis window: low frequencies can be measured, but high
frequencies can not be resolved in time
coarser time
resolution
when? ? ? ?

7. Solution: Wavelet Time-Scale Approach
lower scale
high frequency
convolution
higher scale
low frequency
time(position)
scale

8. Wavelet Mother Functions
must satisfy admissibility conditions (Farge 1992).
Decrease quickly towards 0
Zero mean
Localized in time and frequency domain
Family of shifts and dilations of must allow for
signal reconstruction

9. Analysis of Wavelet Coefficients
Gaussian Wavelet Envelope Descriptor
Statistical Data Summarization Tools
arithmetic mean
geometric mean
harmonic mean
standard deviation
variation
mean absolute deviation
median
interquartile range
range
skewness
Scale Frequency Measure
Percentile Correlations

10. A novel Wavelet Dispersion Measure
time a)
scale b)
rank d) c) e)

11. Performance Wavelet Disp. Measure
Identify pieces of novel
recording of Menuhin 1934
recordings
employed by
the search
system
user query: recording of Menuhin 1934

12. Neural Nets for Audio Classification
training
Perceptron Neural Net
Backpropagation Net
Probabilistic Radial Basis Net
next
slides
answer
user query
Mil 75
Wavelet dispersion vectors
target vectors
Men34 Men57 Hei57
class 1 1
Neural Net
class 2
example 2
vectors
class 32 32
class x

13. Single Layer Perceptron Network
net output
net input 3 b
transfer function
training algorithm
net error

14. Backpropagation Network
Hidden layers and output layer
Different transfer functions
Gradient decent algorithm:
learning rate
minimum

15. Performance Perceptron Net
Perfectly learned example recordings
Was not able to generalize

16. Performance Radial Neural Net
best performance with
biorthogonal wavelet
good performance
with Morlet wavelet

17. Summary
Analysis of Wavelets and Neural Nets for identification of classical movements
Novel Wavelet Dispersion Measure
Novel Methodology with 78 % success rate: a) biorthogonal Wavelet
b) Dispersion Measure
c) Radial Basis Neural Net
Readily applicable for next generation Internet Search Machine

18. Publication & Contact
Pending U.S. patent application: ask Prof. Martin Reisslein for details
Diploma Thesis available at
S. Rein, M. Reisslein, T. Sikora Audio Content Description with Wavelets and Neural Nets, page version submitted to ICASSP’ 04, available on request:

19. Thank You for your help Prof. Dr.-Ing. Thomas Sikora
Prof. Dr. Martin Reisslein
Dr. Nicolas Moreau
Birgit Boldin
Dr.-Ing. Frank Fitzek