Sound is a vital a part of multimodal notion. For a system — be it a voice assistant, a next-generation safety monitor, or an autonomous agent — to behave naturally, it should reveal a full spectrum of auditory capabilities. These capabilities embody transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction.
These various features depend on remodeling uncooked sound into an intermediate illustration, or embedding. However analysis into enhancing the auditory capabilities of multimodal notion fashions has been fragmented, and there stay vital unanswered questions: How can we evaluate efficiency throughout domains like human speech and bioacoustics? What’s the true efficiency potential we’re leaving on the desk? And will a single, general-purpose sound embedding function the inspiration for all these capabilities?
To research these queries and speed up progress towards strong machine sound intelligence, we created the Large Sound Embedding Benchmark (MSEB), offered at NeurIPS 2025.
MSEB gives the mandatory construction to reply these questions by:
- Standardizing analysis for a complete suite of eight real-world capabilities that we consider each human-like clever system should possess.
- Offering an open and extensible framework that permits researchers to seamlessly combine and consider any mannequin kind — from standard downstream uni-modal fashions to cascade fashions to end-to-end multimodal embedding fashions.
- Establishing clear efficiency objectives to objectively spotlight analysis alternatives past present state-of-the-art approaches.
Our preliminary experiments verify that present sound representations are removed from common, revealing substantial efficiency “headroom” (i.e., most enchancment attainable) throughout all eight duties.
