论文标题
关于机器学习的HRTF测量设置之间差异的相关性
On The Relevance Of The Differences Between HRTF Measurement Setups For Machine Learning
论文作者
论文摘要
随着空间音频的流行激增，在其他域中被证明成功的数据驱动的机器学习技术越来越多地用于处理与头部相关的传输功能测量。但是，这些技术需要大量数据，而现有数据集的范围从数十个到低数百个数据点。因此，结合这些数据集的多个，尽管它们是在不同条件下进行测量的。在本文中，我们首先建立了许多数据集之间的共同点，然后研究混合数据集的潜在陷阱。我们执行一个简单的实验，以测试应用机器学习技术时数据集之间剩余差异的相关性。最后，我们指出了最相关的差异。
As spatial audio is enjoying a surge in popularity, data-driven machine learning techniques that have been proven successful in other domains are increasingly used to process head-related transfer function measurements. However, these techniques require much data, whereas the existing datasets are ranging from tens to the low hundreds of datapoints. It therefore becomes attractive to combine multiple of these datasets, although they are measured under different conditions. In this paper, we first establish the common ground between a number of datasets, then we investigate potential pitfalls of mixing datasets. We perform a simple experiment to test the relevance of the remaining differences between datasets when applying machine learning techniques. Finally, we pinpoint the most relevant differences.
