Optimized Learning of Spatial-Fourier Representations from Fast HRIR Recordings

G. Enzner, Christoph Urbanietz, R. Martin
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Abstract

The acquisition of head-related impulse responses (HRIRs) has traditionally been a time-consuming acoustic measurement process. Novel continuous-azimuth recording techniques have dramatically accelerated the acquisition, but conversion into continuous Spatial-Fourier representations (SpaFoR) of HRIRs provides a host of cumbersome implementation challenges. The direct closed-form least-squares approach is unfortunately not practical and we will therefore explore the retrieval of SpaFoR model parameters of HRIR by contemporary machine-learning tools. Specifically, we employ the standard stochastic-gradient learning with Tensorflow on a graphics processing unit (GPU) and compare its performance with previous covariance-based least-squares on the general purpose processor. Apart from the sought simplification and acceleration, our paper is dedicated to hyperparameter optimization in order to make sure the final state of the machine learning approach still attains the accuracy of the optimal least-squares solution. The paper finally applies the proposed method to a real acoustic HRIR recording to illustrate the validity of the system identification obtained by learning.
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快速HRIR记录空间傅里叶表示的优化学习
头部相关脉冲响应(HRIRs)的采集历来是一个耗时的声学测量过程。新的连续方位记录技术极大地加快了采集速度,但将HRIRs转换为连续空间傅里叶表示(SpaFoR)提供了大量繁琐的实现挑战。不幸的是,直接的闭型最小二乘方法并不实用,因此我们将探索用当代机器学习工具检索HRIR的SpaFoR模型参数。具体来说,我们在图形处理单元(GPU)上使用Tensorflow的标准随机梯度学习,并将其性能与先前在通用处理器上基于协方差的最小二乘进行比较。除了寻求简化和加速之外,我们的论文还致力于超参数优化,以确保机器学习方法的最终状态仍然达到最优最小二乘解的精度。最后,将该方法应用于实际的HRIR录音,验证了通过学习得到的系统识别的有效性。
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