论文标题
半监督源的本地化，并具有深层生成建模
Semi-supervised source localization with deep generative modeling
论文作者
论文摘要
我们提出了一种基于用变异自动编码器（VAE）的深层生成建模的半监督定位方法。在混响环境中的本地化仍然是一个挑战，该机器学习（ML）在解决方面表现出了希望。即使有大量数据量，在混响环境中可用于监督学习的标签数量通常也很少。我们通过使用卷积VAE进行半监督学习（SSL）来解决这个问题。对VAE进行了训练，可以在标记和未标记的RTF样品上生成与DOA分类器同时生成相对传递函数（RTF）的相位。将VAE-SSL方法与SRP-PHAT和完全监督的CNN进行了比较。我们发现，在标签受限的方案中，VAE-SSL可以胜过SRP-PHAT和CNN。
We propose a semi-supervised localization approach based on deep generative modeling with variational autoencoders (VAEs). Localization in reverberant environments remains a challenge, which machine learning (ML) has shown promise in addressing. Even with large data volumes, the number of labels available for supervised learning in reverberant environments is usually small. We address this issue by performing semi-supervised learning (SSL) with convolutional VAEs. The VAE is trained to generate the phase of relative transfer functions (RTFs), in parallel with a DOA classifier, on both labeled and unlabeled RTF samples. The VAE-SSL approach is compared with SRP-PHAT and fully-supervised CNNs. We find that VAE-SSL can outperform both SRP-PHAT and CNN in label-limited scenarios.
