研究目的
Investigating the design and implementation of a low-power, event-driven stereo-audio sensing front end inspired by the biological cochlea for smart audio sensing applications.
研究成果
The presented 64×2 channel binaural silicon cochlea demonstrates significant advancements in power efficiency and feature extraction for event-driven stereo-audio sensing. Despite some limitations, the chip's performance in speech reconstruction and vowel discrimination validates its potential for low-power smart audio sensing applications. Future work could focus on improving the PGA design and exploring the tolerance of back-end processing to front-end nonidealities.
研究不足
The current PGA design suffers from peak gain degradation due to insufficient closed-loop bandwidth. The quality of reconstructed speech from event outputs does not yet match that of conventional digital representation enabled by high-precision clocked ADCs.
1:Experimental Design and Method Selection:
The study employs a bioinspired approach to design a silicon cochlea with parallel asynchronous event output. It utilizes source-follower-based bandpass filters (BPFs) and asynchronous delta modulation (ADM) with adaptive self-oscillating comparison for feature extraction and event encoding.
2:Sample Selection and Data Sources:
The chip is fabricated in 0.18 μm 1P6M CMOS, featuring 64 × 2 channels for stereo-audio sensing.
3:18 μm 1P6M CMOS, featuring 64 × 2 channels for stereo-audio sensing.
List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: The chip includes a geometrically scaled bias current generator, translinear loop (TLL) for Q tuning, capacitive attenuator, SFB-BPF, PGA, ADM, and asynchronous logic.
4:Experimental Procedures and Operational Workflow:
The chip's performance is characterized by measuring its power consumption, frequency response, mismatch between channels, and the quality of speech reconstruction from event outputs.
5:Data Analysis Methods:
The analysis includes frequency response measurements, noise power spectral density (PSD), distortion analysis, and speech reconstruction quality assessment.
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