A Comparative Study of Convolutional Neural Networks and Recurrent Neural Networks for Chord Recognition

Abstract

Using Mel-spectrograms, this study evaluates the effectiveness of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs). Mel-spectrograms are justified by their non- linearity and similarity to the human hearing system. This study uses over 200 tracks by The Beatles and Queen collected through the Music Information Retrieval Evaluation Exchange. Data augmentation approaches are used to increase accuracy on unusual chords. This paper presents a 3-layer 2D CNN model trained on major and minor chords and then expanded to different types of chords. The dataset demonstrates that both models can recognize musical chords across various genres. We compare the proposed results to the existing literature and demonstrate the effectiveness of the proposed methodology. As a result of our analysis, we found that the CNN and RNN models were 79% and 76% accurate, respectively. The presented findings suggest that CNNs and RNNs are suitable models for chord recognition using Mel-spectrograms. Data augmentation can be an effective technique for improving accuracy on rare chords.