Musical Key Classification Using Convolutional Neural Network Based on Extended Constant-Q Chromagram

In the field of music information retrieval, musical key classification is one of the challenges. This paper illustrates the advantages of the proposed system with relevant experimental results, starting with diverse audio datasets for feature extraction used for training and testing a classification model which is based on a convolutional neural network (CNN). The goal is to develop a feature that can improve the neural network's performance. To compare the effect of input features on efficiency, a basic CNN is trained from the ground up and utilized as an image classification tool. The Chromagram-24, an augmented version of the input chroma feature, is proposed to improve the accuracy of musical key detection. In terms of weighted score, the model using Chromagram-24 as an input feature outperforms the model trained using a conventional 12-dimensional chromagram by 12.77% and achieves the highest score of 85.63% when classifying full-length songs. Chromagrams are generated using audio excerpts ranging in length from 15 to 60 seconds for local key estimation, whereas, for global key estimation, a full-length audio set is used. The results indicate that, given the different lengths of training audio input, executing the model using a chromagram of a 60-second audio excerpt yields the best results.