Improving the localization accuracy of virtual sound source through reinforcement learning
Improving the localization accuracy of virtual sound source through reinforcement learning
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通过强化学习提高虚拟声源定位精度
DOI：
10.1109/smc.2013.747
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Y.
中科院分区：
文献类型：
--
作者：
Washizu;M.;Morioka;S.;Nambu;I.;Yano;S.;Hokari;H.;Wada;Y.
虚拟声源本地化是一种使用立体声耳机再现三维声音的技术，该技术在使用听觉刺激的脑机接口中的应用正在被研究中。为了使用这种技术实现虚拟声音，必须准确测量用户的头部相关传递函数(HRTF)。通过适当放置麦克风和测量环境，可以准确地测量HRTF，但通常很难获得理想的设置。为了克服这一点，我们尝试使用强化学习来获得准确的HRTF。我们进行了仿真，并验证了该方法在24个水平方向上提高了HRTF精度。另外，在在线学习实验中，3个被试的定位精度都得到了提高，表明了该方法的有效性。
Localization of virtual sound source is a technology that allows the reproducing of three-dimensional sounds using stereo earphones, and applications of this technology in Brain-machine interface that use auditory stimuli are being investigated. In order to achieve virtual sounds using this technology, the Head-related Transfer Function (HRTF) of the user must be measured accurately. The HRTF can be measured accurately with the appropriate placement of the microphones and measurement environment, but procuring an ideal setup is usually difficult. To overcome this, we instead attempt to obtain an accurate HRTF using reinforcement learning. We performed simulations and verified that with the proposed method the HRTF accuracy improved on 24 horizontal directions. Also, in online learning experiments, the localization accuracy was improved for 3 subjects, suggesting the validity of our method.