Ziquan Qin

About

Ziquan Qin is a rising researcher at the intersection of embedded systems, robot audition, and low-power signal processing. His work focuses on enabling real-time, energy-efficient audio processing for autonomous robots, particularly through hardware acceleration on FPGA platforms. Qin’s most cited paper, "Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board" (2023, 4 citations), demonstrates a practical pathway to deploying complex source separation algorithms—traditionally computationally intensive—on resource-constrained devices. This contribution is part of the broader HARK (Honda Research Institute Japan Audition for Robots with Kyoto University) open-source software framework, which aims to become the “OpenCV of audio” by offering modular tools for sound localization, separation, and automatic speech recognition. By reducing power consumption while maintaining algorithmic fidelity, Qin’s work directly addresses a critical bottleneck in mobile robotics: the need for robust auditory perception without draining battery life. His achievements signal a promising trajectory in making robot hearing both accessible and deployable in real-world, low-power environments—a key step toward truly autonomous interactive machines.

Research Focus

Key Achievements

Top Papers

1. 1
   Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

   4 citations · 2023

Key Collaborators

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