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Acappella comprises around 46 hours of a cappella solo singing videos sourced from YouTbe, sampled across different singers and languages. Four languages are considered: English, Spanish, Hindi and others. This dateset was designed for Audiovisual singing voice separation, although it can be used for any self-supervised audio-visual task such us: audio-guided lip reading, audio-visual learning,
The dataset uses VGG-Sound which consists of 10s clips collected from YouTube for 309 sound classes. A subset of ‘temporally sparse’ classes is selected using the following procedure: 5–15 videos are randomly picked from each of the 309 VGGSound classes, and manually annotated as to whether audio-visual cues are only sparsely available. As a result, 12 classes are selected (∼4 %) or 6.5k and 0.6k videos in the train and test sets, respectively. The classes include 'dog barking', 'chopping wood', 'lion roaring', 'skateboarding' etc.
AVSync15 is a high-quality synchronized audio-video dataset curated from VGGSound. It is carefully curated with both automatic and manual steps, ensuring: High semantic and temporal audio-visual correlation Clean and stable audio-visual contents Rich in audio-video synchronization clues Diverse in categories
Provided in the linked paper.
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