论文标题
SSLIDE：基于深度学习的室内的声源本地化
SSLIDE: Sound Source Localization for Indoors based on Deep Learning
论文作者
论文摘要
本文使用深度学习介绍了室内的SSLIDE，声源本地化，该室内的深度学习应用了带有编码器解码器结构的深神经网络（DNN），以将声源定位在连续空间中具有随机位置的声音源。每个麦克风收到的声音信号的空间特征被提取，并表示为每个点的声源位置的似然表面。我们的DNN由一个编码器网络组成，然后是两个解码器。编码器获得了输入可能性的压缩表示。一个解码器可以解决由混响引起的多径，另一个解码器估计了源位置。基于模拟和实验数据的实验表明，我们的方法不仅可以超越多个信号分类（音乐），具有相变（SRP-PHAT）（SRP-PHAT），稀疏的贝叶斯学习（SBL）和竞争性卷积神经网络（CNN）方法在混响环境中，还可以实现良好的一般概括性能。
This paper presents SSLIDE, Sound Source Localization for Indoors using DEep learning, which applies deep neural networks (DNNs) with encoder-decoder structure to localize sound sources with random positions in a continuous space. The spatial features of sound signals received by each microphone are extracted and represented as likelihood surfaces for the sound source locations in each point. Our DNN consists of an encoder network followed by two decoders. The encoder obtains a compressed representation of the input likelihoods. One decoder resolves the multipath caused by reverberation, and the other decoder estimates the source location. Experiments based on both the simulated and experimental data show that our method can not only outperform multiple signal classification (MUSIC), steered response power with phase transform (SRP-PHAT), sparse Bayesian learning (SBL), and a competing convolutional neural network (CNN) approach in the reverberant environment but also achieve a good generalization performance.
