Music Chord Recognition Based on Midi Trained Deep Feature and BLSTM CRF Hybird Decoding
In this paper, we design a novel deep learning based hybrid system for automatic chord recognition. Currently, there is a bottleneck in the amount of enough annotated data for training robust acoustic models, as hand annotating time synchronized chord labels requires professional musical skills and considerable labor. As a solution to this problem, we construct a large set of time synchronized MIDI audio pairs, and use these data to train a Deep Residual Network (DRN) feature extractor, which can then estimate pitch class activations of real world music audio recordings. Sequence classification and decoding are then performed with a trained Bidirectional LSTM and Conditional Random Fields (CRF) network. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, the performance is good and no less than other state of the art systems. The proposed system also achieved good evaluation score in MIREX 2017 Automatic Chord Estimation task.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)