Massive Sound Embedding Benchmark (MSEB)

2026-02-06

AI Summary

Current multimodal systems lack a unified benchmark for comprehensively evaluating auditory capabilities such as transcription, classification, retrieval, and reasoning. To address this gap, this work proposes the first unified evaluation framework for audio embeddings that spans a diverse set of auditory tasks, encompassing eight core benchmarks. The framework features a modular design that integrates heterogeneous data sources and multiple task types, enabling scalable and extensible assessment. Accompanying this framework, we release Simple Voice Questions (SVQ), a large-scale open-source dataset. Preliminary experiments reveal significant performance bottlenecks of existing methods in real-world scenarios, establishing a standardized platform and clear direction for advancing machine auditory perception in multimodal systems.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, each task involves transforming a raw audio signal into a meaningful'embedding'- be it a single vector, a sequence of continuous or discrete representations, or another structured form - which then serves as the basis for generating the task's final response. To accelerate progress towards robust machine auditory intelligence, we present the Massive Sound Embedding Benchmark (MSEB): an extensible framework designed to evaluate the auditory components of any multimodal system. In its first release, MSEB offers a comprehensive suite of eight core tasks, with more planned for the future, supported by diverse datasets, including the new, large-scale Simple Voice Questions (SVQ) dataset. Our initial experiments establish clear performance headrooms, highlighting the significant opportunity to improve real-world multimodal experiences where audio is a core signal. We encourage the research community to use MSEB to assess their algorithms and contribute to its growth. The library is publicly hosted at github.

Problem

• audio embedding
• multimodal perception
• auditory intelligence
• benchmark
• sound understanding

Innovation

• audio embedding
• multimodal benchmark
• machine auditory intelligence
• Simple Voice Questions
• MSEB