Recurrent Dilated DenseNets for a Time-Series Segmentation Task

Abstract

Efficient real-time segmentation and classification of time-series data is key in many applications, including sound and measurement analysis. We propose an efficient convolutional recurrent neural network (CRNN) architecture that is able to deliver improved segmentation performance at lower computational cost than plain RNN methods. We develop a CNN architecture, using dilated DenseNet-like kernels and implement it within the proposed CRNN architecture. For the task of online wafer-edge measurement analysis, we compare our proposed methods to standard RNN methods, such as Long Short Term Memory (LSTM) and Gated Recurrent Units (GRUs). We focus on small models with a low computational complexity, in order to run our model on an embedded device. We show that frame-based methods generally perform better than RNNs in our segmentation task and that our proposed recurrent dilated DenseNet achieves a substantial improvement of over 1.1 % F1-score compared to other frame-based methods.