Audio Tracking in Noisy Environments by Acoustic Map and Spectral Signature
提出一种利用麦克风阵列在强噪声环境中跟踪目标的方法，通过机器学习计算频谱分类图并与声学地图结合，去除干扰源后使用粒子滤波估计目标位置，在说话人和车辆跟踪实验中性能显著提升。
A novel method is proposed for generic target tracking by audio measurements from a microphone array. To cope with noisy environments characterized by persistent and high energy interfering sources, a classification map (CM) based on spectral signatures is calculated by means of a machine learning algorithm. Next, the CM is combined with the acoustic map, describing the spatial distribution of sound energy, in order to obtain a cleaned joint map in which contributions from the disturbing sources are removed. A likelihood function is derived from this map and fed to a particle filter yielding the target location estimation on the acoustic image. The method is tested on two real environments, addressing both speaker and vehicle tracking. The comparison with a couple of trackers, relying on the acoustic map only, shows a sharp improvement in performance, paving the way to the application of audio tracking in real challenging environments.