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Real-time implementation of a GMM-based binaural localization algorithm on a VLIW-SIMD processor

机译:在VLIW-SIMD处理器上实时实现基于GMM的双耳定位算法

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Localization algorithms have become of considerable interest for robot audition, acoustic navigation, teleconferencing, speaker localization, and many other applications over the last decade. In this paper, we present a real-time implementation of a Gaussian mixture model (GMM) based probabilistic sound source localization algorithm for a low-power VLIW-SIMD processor for hearing devices. The algorithm has been proven to allow for robust localization of multiple sound sources simultaneously in reverberant and noisy environments. Real-time computation for audio frames of 512 samples at 16 kHz was achieved by introducing algorithmic optimizations and hardware customizations. To the best of our knowledge, this is the first real-time capable implementation of a computationally complex GMM-based sound source localization algorithm on a low-power processor. The resulting estimated core area without consideration of memory in 40nm low-power TSMC technology is 188,511 pm.
机译:在过去的十年中,本地化算法已成为机器人试听,声学导航,电话会议,演讲者本地化和许多其他应用程序的极大兴趣。在本文中,我们为听力设备的低功耗VLIW-SIMD处理器提供了基于高斯混合模型(GMM)的概率性声源定位算法的实时实现。该算法已被证明可以在混响和嘈杂的环境中同时对多个声源进行稳健的定位。通过引入算法优化和硬件定制,可以实时计算512个采样频率为16 kHz的音频帧。据我们所知,这是在低功率处理器上第一个实时实现基于计算复杂的基于GMM的声源定位算法的功能。在不考虑内存的情况下,采用40nm低功耗TSMC技术得出的估计核心面积为188,511 pm。

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