VocalSound

A dataset for improving human vocal sounds recognition, containing over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Used for audio event classification and recognition of human non-speech vocalizations.

Qwen2.5-Omni-7B from Alibaba Cloud / Qwen Team currently leads the VocalSound leaderboard with a score of 0.939 across 1 evaluated AI models.

Paper

Alibaba Cloud / Qwen TeamQwen2.5-Omni-7B leads with 93.9%.

Progress Over Time

Interactive timeline showing model performance evolution on VocalSound

State-of-the-art frontier
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VocalSound Leaderboard

1 models
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1
Alibaba Cloud / Qwen Team
Alibaba Cloud / Qwen Team
7B
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FAQ

Common questions about VocalSound.

What is the VocalSound benchmark?

A dataset for improving human vocal sounds recognition, containing over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Used for audio event classification and recognition of human non-speech vocalizations.

What is the VocalSound leaderboard?

The VocalSound leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, Qwen2.5-Omni-7B by Alibaba Cloud / Qwen Team leads with a score of 0.939. The average score across all models is 0.939.

What is the highest VocalSound score?

The highest VocalSound score is 0.939, achieved by Qwen2.5-Omni-7B from Alibaba Cloud / Qwen Team.

How many models are evaluated on VocalSound?

1 models have been evaluated on the VocalSound benchmark, with 0 verified results and 1 self-reported results.

Where can I find the VocalSound paper?

The VocalSound paper is available at https://arxiv.org/abs/2205.03433. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does VocalSound cover?

VocalSound is categorized under audio. The benchmark evaluates audio models.

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