Music Classification using an Improved CRNN with Multi-Directional Spatial Dependencies in Both Time and Frequency Dimensions
Zhen Wang, Suresh Muknahallipatna, Maohong Fan, Austin Okray, Chao Lan · 2019
In music classification tasks, Convolutional Recurrent Neural Network (CRNN) has achieved state-of-the-art performance on several data sets. However, the current CRNN technique only uses RNN to extract spatial dependency of music signal in its time dimension but not its frequency dimension. We hypothesize the latter can be additionally exploited to improve classification performance. In this paper, we propose an improved technique called CRNN in Time and Frequency dimensions (CRNN-TF), which captures spatial dependencies of music signal in both time and frequency dimensions in multiple directions. Experimental studies on three real-world music data sets show that CRNN-TF consistently outperforms CRNN and several other state-of-the-art deep learning-based music classifiers. Our results also suggest CRNN-TF is transferable on small music data sets via the fine-tuning technique.