Accelerating Audio Research with Robotic Dummy Heads
Austin Lu, Kanad Sarkar, Yongjie Zhuang, Leo Lin, Ryan M Corey, Andrew C Singer
TL;DR
The paper addresses the bottleneck of slow, non-repeatable data collection in spatial audio research by introducing a robotic acoustic dummy head that moves quietly while recording binaural audio. It combines a 3D-printed head with a quiet, direct-drive motion platform to deliver realistic HRTFs and repeatable motion, validated against a KEMAR and demonstrated in moving-talker beamforming experiments using MVDR+CW. Key contributions include open-source design files, validated HRTF realism, and evidence of repeatable dynamic recordings suitable for objective evaluation of motion-aware algorithms, with potential to enable large-scale labeled spatial audio datasets. This tool promises to accelerate audio research across localization, tracking, source separation, and cocktail-party problem domains by enabling scalable, motion-enabled data collection without prohibitive costs or noise artifacts.
Abstract
This work introduces a robotic dummy head that fuses the acoustic realism of conventional audiological mannequins with the mobility of robots. The proposed device is capable of moving, talking, and listening as people do, and can be used to automate spatially-stationary audio experiments, thus accelerating the pace of audio research. Critically, the device may also be used as a moving sound source in dynamic experiments, due to its quiet motor. This feature differentiates our work from previous robotic acoustic research platforms. Validation that the robot enables high quality audio data collection is provided through various experiments and acoustic measurements. These experiments also demonstrate how the robot might be used to study adaptive binaural beamforming. Design files are provided as open-source to stimulate novel audio research.
