AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence
This dataset addresses a data gap for researchers in audio processing, though it is incremental as it builds on an existing dataset.
The authors tackled the lack of open-source datasets with strong polyphonic labels for speech and music activity detection by creating AVASpeech-SMAD, which extends an existing 45-hour dataset with frame-level music labels and provides benchmark results from two state-of-the-art systems.
We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research. With frame-level music labels, the proposed dataset extends the existing AVASpeech dataset, which originally consists of 45 hours of audio and speech activity labels. To the best of our knowledge, the proposed AVASpeech-SMAD is the first open-source dataset that features strong polyphonic labels for both music and speech. The dataset was manually annotated and verified via an iterative cross-checking process. A simple automatic examination was also implemented to further improve the quality of the labels. Evaluation results from two state-of-the-art SMAD systems are also provided as a benchmark for future reference.