Sound plays a crucial role in multimodal perception, essential for systems like voice assistants and autonomous agents to function naturally. These systems require a wide range of auditory capabilities, including transcription, classification, and reasoning, which depend on transforming raw sound into an intermediate representation known as embedding. However, research in this area has been fragmented, with key questions about cross-domain performance and the potential for a universal sound embedding remaining unanswered. To address these challenges, the Massive Sound Embedding Benchmark (MSEB) was introduced, providing a standardized evaluation framework for eight critical auditory capabilities. This benchmark aims to unify research efforts by allowing seamless integration and evaluation of various model types, setting clear performance goals to identify opportunities for advancement beyond current technologies. Initial findings indicate significant potential for improvement across all tasks, suggesting that existing sound representations are not yet universal. This matters because enhancing auditory intelligence in machines can lead to more effective and natural interactions in numerous applications, from personal assistants to security systems.
