Residual DNN-CRF Model for Audio Chord Recognition
Shota Nakayama, Shuichi Arai · 2017
In this paper, we propose Residual DNN-CRF Model for Audio Chord Recognition. The network architecture of chord recognition using deep learning so far consists of a shallow network of about three layers. Even if Convolutional Neural Network is used, if the hidden layer in the network architecture is shallow, the original power of deep learning will not be demonstrated. Of course it is the same for DNN. Therefore, we propose a network architecture with 15 hidden layers. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. We provide superior results by building deeper network architecture than before. In addition, we compare the results of the proposed model with the results of the Robbie Williams dataset used in other chord recognition network architectures.