用手机自校准电脑音箱，3分钟实现在线听力研究的精准发声
EasyEyes: Online hearing research using speakers calibrated by phones
- 用手机麦克风建模，通过非同步最大长度序列算法校准电脑音箱
- 校准后频响标准差小于3 dB，接近平坦响应，满足听力实验精度要求
- 开源库支持87%美国和80%英国用户，适合大规模在线听力研究
听力研究需要经过校准的声音源，传统上依赖实验室设备。在线研究更快速且包容性强，但多数参与者缺乏校准设备，声音输出设备各异且未校准。本文介绍开源工具EasyEyes.app如何实现在线音箱校准：利用智能手机麦克风的预存声学模型，用户仅需选择手机型号（通过屏幕尺寸验证），3分钟内即可完成电脑扬声器校准。校准采用Novak等人提出的非同步最大长度序列（MLS）算法，通过将输入信号与扬声器脉冲响应的逆卷积进行修正。研究人员可使用测量麦克风贡献新手机模型至开放共享库，每个手机模型均追溯至原始测量麦克风制造商数据。校准后播放平坦谱的MLS信号，其输出频谱标准差低于3 dB，接近理想平坦。调查显示，94款主流品牌手机模型覆盖了美国87%、英国80%的潜在参与者，有效支撑大规模在线听力研究。
原文摘要 · Abstract (English)
Hearing research requires a calibrated sound source, traditionally as lab equipment. Online research is quicker and more inclusive, but most participants lack calibration equipment and their sound sources are uncalibrated and diverse. This article explains how the open-source EasyEyes.app calibrates loudspeakers online. A library of smartphone-microphone profiles allows EasyEyes to use the participant's phone to calibrate their computer's loudspeaker in three minutes. Participants select their phone model, which is verified by screen size. Calibration employs the Novak et al. nonsynchronous maximum-length-sequence (MLS) algorithm. The computer's loudspeaker is corrected by convolving its input with the inverse of its impulse response. Researchers can contribute to the open-access library by calibrating phones with a measurement microphone. In the library, each profile is linked back to the profile used to produce it, back to the manufacturer profile of a measurement microphone. Correction accuracy is such that playing the flat-spectrum MLS through the corrected loudspeaker produces a nearly flat spectrum, with standard deviation less than 3 dB. A survey shows that a library of 94 phone models from major brands will support most participants in the USA (87%) and UK (80%). This method facilitates efficient and inclusive online hearing research.
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