Researchers have created the Massive Sound Embedding Benchmark (MSEB), presented at NeurIPS 2025, to evaluate how well machine systems handle sound across a range of real-world tasks. The benchmark is aimed at improving multimodal perception models used in systems such as voice assistants, security monitors, and autonomous agents.
The work focuses on the need to transform raw sound into an intermediate representation, or embedding, so models can support tasks including transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction.
The researchers said work on auditory capabilities has been fragmented and that important questions remain about how to compare performance across domains like human speech and bioacoustics, how much performance remains untapped, and whether a single, general-purpose sound embedding could support all of these capabilities.
MSEB is designed to address those questions by standardizing evaluation across eight real-world capabilities, providing an open and extensible framework for different model types, and setting clear performance goals to show where current approaches fall short.
According to the source, initial experiments found that current sound representations are far from universal and that there is substantial performance “headroom” across all eight tasks.
Source: research.google.
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