sound is an important part multimodal perceptionFor a system – whether it’s a voice assistant, a next-generation security monitor, or an autonomous agent – to behave naturally, it must exhibit the full spectrum of listening capabilities, These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction,
These diverse functions rely on converting raw sound into an intermediate representation, or embeddingBut research into improving the auditory capabilities of multimodal perception models has been fragmented, and important unanswered questions remain: How do we compare performance across domains such as human speech and bioacoustics? What is Truth Performance potential are we leaving on the table? And could a single, general-purpose sound embedding serve as the foundation for all these capabilities?
To investigate these questions and accelerate progress toward strong machine sound intelligence, we created mammoth sound embedding benchmark (MSEB), presented at NeuroIPS 2025,
MSEB provides the structure needed to answer these questions:
- Standardized assessment for a comprehensive suite of eight real-world capabilities that we believe every human-like intelligent system should possess.
- Providing an open and extensible framework that allows researchers to seamlessly integrate and evaluate any model type – from traditional downstream uni-modal models to cascade models to end-to-end multimodal embedding models.
- Establish clear performance targets to objectively highlight research opportunities beyond current state-of-the-art approaches.
Our preliminary experiments confirm that the current sound representation is far from universal, suggesting substantial performance “headroom” (i.e., the maximum improvement possible) across all eight tasks.
