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

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

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.

Design of Filter Bank (2) Fully Cascaded BPF (3) TSFB (1) Lyon & Mead H 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:

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

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

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

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

Shamma’s Model The Stereausis Network

Stereausis Processor

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

Binaural Processing with TSFB

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

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

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

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.