Training chord recognition models on artificially generated audio
This addresses data scarcity in Music Information Retrieval for researchers and practitioners, but it is incremental as it builds on existing methods with new data combinations.
The study tackled the problem of limited non-copyrighted audio for training chord recognition models by comparing Transformer-based models trained on artificially generated audio, finding that such data can enrich smaller human-composed datasets or serve as a standalone set for pop music chord prediction.
One of the challenging problems in Music Information Retrieval is the acquisition of enough non-copyrighted audio recordings for model training and evaluation. This study compares two Transformer-based neural network models for chord sequence recognition in audio recordings and examines the effectiveness of using an artificially generated dataset for this purpose. The models are trained on various combinations of Artificial Audio Multitracks (AAM), Schubert's Winterreise Dataset, and the McGill Billboard Dataset and evaluated with three metrics: Root, MajMin and Chord Content Metric (CCM). The experiments prove that even though there are certainly differences in complexity and structure between artificially generated and human-composed music, the former can be useful in certain scenarios. Specifically, AAM can enrich a smaller training dataset of music composed by a human or can even be used as a standalone training set for a model that predicts chord sequences in pop music, if no other data is available.