A Fully Convolutional Deep Auditory Model for Musical Chord Recognition

Abstract

Chord recognition systems depend on robust feature extraction pipelines. While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks. In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction. The extracted features are processed by a Conditional Random Field that decodes the final chord sequence. Both processing stages are trained automatically and do not require expert knowledge for optimising parameters. We show that the learned auditory system extracts musically interpretable features, and that the proposed chord recognition system achieves results on par or better than state-of-the-art algorithms.

In Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), Vietro sul Mare, Italy

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• Using musical relationships between chord labels in automatic chord extraction tasks
• Genre-Agnostic Key Classification With Convolutional Neural Networks
• Large-Scale Classification of Structured Objects using a CRF with Deep Class Embedding
• Improved Chord Recognition by Combining Duration and Harmonic Language Models
• A Bi-directional Transformer for Musical Chord Recognition
• Modeling Music Modality with a Key-Class Invariant Pitch Chroma CNN
• Chord Recognition- Music and Audio Information Retrieval
• Automatic Chord Recognition with Higher-Order Harmonic Language Modelling
• Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
• Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network