1. A Model of Binaural Processing Based on Tree-Structure Filter-Bank
길이만, 김영익, 김화길, 구임회
한국과학기술원 응용수학전공

2. Motivation
Design of auditory preprocessors motivated from the characteristics of biological auditory systems. - robustness to noise - capturing the minute differences between signals (2 Hz difference) - wide dynamic range (140 dB) - selective attention - source localization using two ears

3. Design of Basilar Membrane (BM)
Types of BM Models
Lyon and Mead
- R. F. Lyon and C. Mead, An Analog Electronic Cochlea, IEEE Transactions
on Acoustics, Speech and Signal Processing, 37(7), 1988.
Liu
- W. Liu, A. G. Andreou, and Jr. M. H. Goldstein, Voiced-Speech Representation
by an Analog Silicon Model of the Auditory Periphery, IEEE Transactions
on Neural Network, 3(3), 1992.
Kates
- J. M. Kates, A Time-Domain Digital Cochlear Model, IEEE Transaction on
Signal Processing, 39(12), 1991.
Hamming BPF
- O. Ghitza, Robustness against Noise: the Role of Timing-Synchrony Measurement.
IEEE International Conference on Acoustics, Speech and Audio Processing, 6.8, 1987.

4. Design of Filter Bank (2) Fully Cascaded BPF (3) TSFB (1) Lyon & MeadH L H L
(1) Lyon & Mead L
Cascaded LPFs
Number of Filters:
Cascaded LPFs & HPFs
Higher bandpass capability
Equal delay time
Number of Filters:
Tree sructure
Cascaded LPFs & HPF
Higher bandpass capability
Equal delay time
Versatile Q control
Number of Filters:

5. Binaural Processing Models
EE (Excitation-Excitation) cells in medial superior olive (MSO) - interaural cross-correlation models
EI (Excitation-Inhibition) cells in lateral superior olive (LSO) - equalization-cancellation (EC) theory

6. Interaural Cross-correlation Model (EE-type cells)
Running interaural cross-correlation (Jeffress, 1948)
Delay weighting (Colburn, 1977)
Frequency weighting (Stern and Shear, 1996)

7. Lindemann’s Model (EI-type cells)
Contralateral inhibition mechanism
Stationary-inhibition component
Dynamic-inhibition component

8. Breebaart Model (EI-type cells)
Combined EI-type cell
Temporal windowing
Nonlinear saturation

9. Shamma’s Model
The Stereausis Network

10. Stereausis Processor

11. Network output for time shifted 600Hz tone a) zero shift b) shift c) shift d) shift

12. Binaural Processing with TSFB

13. Simulation for Binaural Processing
- Signal : TI46 (‘zero’ ~ ‘nine’) male speech samples
- Noise : Noisex samples

14. Simulation for Binaural Processing
Feature
ZCPA 45 90
White
Gaussian
Noise 10
94.3
95.0
95.3
94.1
94.5 5
85.9
93.0
94.2
92.2
92.5
49.7
63.7
74.3
63.9
68.2
75.9 Op
Room
94.8
94.9
95.6
93.3
93.7
94.4
74.2
87.5
89.6
88.7
91.3
88.8
F16
94.7
95.2
90.7
93.5
93.6
92.1
51.0
67.9
71.2
66.4
64.7

15. Simulation for Monaural Processing without HRTF
Feature
ZCPA
White
Gaussian
Noise 10
97.4
95.8 5
96.8
95.1
82.4
86.2 Op
Room
95.7
96.0
94.9
76.8
91.2
F16
97.3
96.9
83.5
87.5

16. Conclusion
A model of binaural processing with TSFB has been suggested.
Simulation results showed that the binaural processing could be advantageous in noisy environment.
The HRTF could degrade the performance of speech recognition.
A new feature combining binaural data will be investigated in the sense of noise robustness.