用仿真数据训练模型，提升麦克风阵列定位精度
LocaGen: Sub-Sample Time-Delay Learning for Beam Localization
- 通过仿真生成数据训练模型，降低采样量化误差
- 在10kHz采样率下，方向定位误差降低约67%
- 适合资源受限的嵌入式设备实时应用
LocaGen旨在提升二维声源波束定位性能。该系统通过在真实感仿真数据上训练机器学习模型，减少采样量化误差，从而提高由三麦克风阵列实现的方向到达（DOA）及精确位置估计的准确性。实验表明，在低功耗嵌入式系统上，该方法在几乎不增加实时资源消耗的前提下，显著提升了定位精度。即使在仅10kHz音频处理采样率下，仍可实现约67%的DOA误差降低。
原文摘要 · Abstract (English)
The goal of LocaGen is to improve the localization performance of audio signals in the 2-D beam localization problem. LocaGen reduces sampling quantization errors through machine learning models trained on realistic synthetic data generated by a simulation. The system increases the accuracy of both direction-of-arrival (DOA) and precise location estimation of an audio beam from an array of three microphones. We demonstrate LocaGen's efficacy on a low-powered embedded system with an increased localization accuracy with a minimal increase in real-time resource usage. LocaGen was demonstrated to reduce DOA error by approximately 67% even with a microphone array of only 10 kHz in audio processing.
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