Automatic Audio Chord Recognition With MIDI Trained Deep Feature and BLSTM CRF Sequence Decoding Model
With the advances of machine learning technologies, data driven feature extraction and sequence modeling approaches are being widely explored for automatic chord recognition tasks. 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. To cope with this limitation, in this paper, we propose a convolutional neural network (CNN) based deep feature extractor, which is trained on a large set of time, synchronized musical instrument digital interface audio data pairs and can robustly estimate pitch class activations of real world music audio recordings. The CNN feature extractor plus a bidirectional long short term memory conditional random field decoding model forms the proposed hybrid system for automatic chord recognition. Experiments show that the proposed model is compatible for both regular major/minor triad chord classification and larger vocabulary chord recognition, and outperforms other state of the art chord recognition systems.
IEEE/ACM Transactions on Audio, Speech, and Language Processing