Open Broadcast Media Audio from TV: A Dataset of TV Broadcast Audio with Relative Music Loudness Annotations

Abstract

Open Broadcast Media Audio from TV (OpenBMAT) is an open, annotated dataset for the task of music detection that contains over 27 hours of TV broadcast audio from 4 countries distributed over 1647 one-minute long excerpts. It is designed to encompass several essential features for any music detection dataset and is the first one to include annotations about the loudness of music in relation to other simultaneous non-music sounds. OpenBMAT has been cross-annotated by 3 annotators obtaining high inter-annotator agreement percentages, which allows us to validate the annotation methodology and ensure the annotations reliability. In this work, we first review the current publicly available music detection datasets and state OpenBMAT’s contributions. After that, we detail its building process: the selection of the audio and the annotation methodology. Then, we analyze the produced annotations and validate their reliability. We continue with an experiment to highlight the value of these annotations and investigate the most challenging content in OpenBMAT. Finally, we describe the details about the format in which the dataset is presented and the platform where we have made it available. We believe OpenBMAT will contribute to major advancements of the research on music detection in real-life scenarios.

Keywords

• dataset
• music detection
• music relative loudness
• automatic classification
• inter-annotator agreement
• TV broadcast audio