METER2800: A novel dataset for music time signature detection

Abstract

The Meter2800 dataset is an important contribution to Music Information Retrieval (MIR) research, as it is the first dataset to include audio files specifically designed for time signature detection. By combining audio files from three renowned datasets and including additional tracks, we have created a comprehensive and diverse collection of 2800 audio tracks that overcomes the limitations of existing audio datasets. The dataset includes 2.26GB of high-quality audio, which has been annotated with metadata, pre-computed features, tempo and time signature. In addition, we propose a train/test split and provide baseline results for time signature detection. The dataset is freely available for the research community and is available online for download. We believe that Meter2800 will contribute to the advancement of Music Information Retrieval research, particularly in the area of time signature detection. In technical validation, four classification experiments were conducted using four types of machine learning algorithms: SVM, KNN, Naive Bayes, and Random Forest.