A deep learning method for enforcing coherence in Automatic Chord Recognition
Gianluca Micchi, Katerina Kosta, Gabriele Medeot, Pierre Chanquion · Zenodo (CERN European Organization for Nuclear Research) · 2021
Deep learning approaches to automatic chord recognition and functional harmonic analysis of symbolic music have improved the state of the art, but they still face a common problem: how to deal with a vast chord vocabulary. The naive approach of writing one output class for each possible chord is hindered by the combinatorial explosion of the output size (~10 million classes). We can reduce this complexity by several orders of magnitude by treating each label (e.g. key or chord quality) independently. However this has been shown to lead to incoherent output labels. To solve this issue we introduce a modified Neural Autoregressive Distribution Estimation (NADE) as the last layer of a Convolutional Recurrent Neural Network. The NADE layer ensures that labels related to the same chord are dependently predicted, therefore enforcing coherence. The experiments showcase the advantage of the new model both in automatic chord recognition and functional harmonic analysis compared to the model that does not include NADE as well as State of the Art models.