DAACI-VoDAn: Improving Vocal Detection with New Data and Methods
Helena Cuesta, Nadine Kroher, Aggelos Pikrakis, Stojan Djordjevic · 2023
Vocal detection (VD) algorithms aim to detect the presence of vocals in music recordings and are an essential preprocessing step for other tasks, including singer identification and lyrics transcription. However, the lack of large-scale annotated datasets has slowed down research in the field, in particular w.r.t. the application of modern deep learning methods. This paper introduces DAACI-VoDAn, a novel dataset for VD that contains 706 full-length music tracks and vocal segment annotations. In addition, we propose a new method for the task that outperforms state of the art methods on DAACI-VoDAn as well as on an existing VD dataset. Our approach combines a convolutional head, that is pre-trained on large amounts of weakly-labeled data, with a temporal-convolutional architecture which models the occurrence of two-dimensional patterns over time.