Back to Search
Start Over
Spiking Cochlea with System-level Local Automatic Gain Control
- Publication Year :
- 2022
-
Abstract
- Including local automatic gain control (AGC) circuitry into a silicon cochlea design has been challenging because of transistor mismatch and model complexity. To address this, we present an alternative system-level algorithm that implements channel-specific AGC in a silicon spiking cochlea by measuring the output spike activity of individual channels. The bandpass filter gain of a channel is adapted dynamically to the input amplitude so that the average output spike rate stays within a defined range. Because this AGC mechanism only needs counting and adding operations, it can be implemented at low hardware cost in a future design. We evaluate the impact of the local AGC algorithm on a classification task where the input signal varies over 32 dB input range. Two classifier types receiving cochlea spike features were tested on a speech versus noise classification task. The logistic regression classifier achieves an average of 6% improvement and 40.8% relative improvement in accuracy when the AGC is enabled. The deep neural network classifier shows a similar improvement for the AGC case and achieves a higher mean accuracy of 96% compared to the best accuracy of 91% from the logistic regression classifier.<br />Comment: Accepted for publication at the IEEE Transactions on Circuits and Systems I - Regular Papers, 2022
- Subjects :
- Electrical Engineering and Systems Science - Signal Processing
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Machine Learning
Computer Science - Sound
Electrical Engineering and Systems Science - Audio and Speech Processing
Electrical Engineering and Systems Science - Systems and Control
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2202.06707
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/TCSI.2022.3150165