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X-ARES: A Comprehensive Framework for Assessing Audio Encoder Performance

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arxiv 2505.16369 v2 pith:CHVGOW5L submitted 2025-05-22 cs.SD eess.AS

X-ARES: A Comprehensive Framework for Assessing Audio Encoder Performance

Junbo Zhang , Heinrich Dinkel , Yadong Niu , Chenyu Liu , Si Cheng , Anbei Zhao , Jian Luan This is my paper
classification cs.SD eess.AS
keywords audioevaluationperformancetasksx-aresacrossdomainsencoder
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduces X-ARES (eXtensive Audio Representation and Evaluation Suite), a novel open-source benchmark designed to systematically assess audio encoder performance across diverse domains. By encompassing tasks spanning speech, environmental sounds, and music, X-ARES provides two evaluation approaches for evaluating audio representations: linear fine-tuning and unparameterized evaluation. The framework includes 22 distinct tasks that cover essential aspects of audio processing, from speech recognition and emotion detection to sound event classification and music genre identification. Our extensive evaluation of state-of-the-art audio encoders reveals significant performance variations across different tasks and domains, highlighting the complexity of general audio representation learning.

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Cited by 3 Pith papers

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    AU-Harness introduces an efficient unified evaluation framework for audio LLMs featuring batch optimizations, multi-turn dialogue support, and standardized protocols for fair comparisons.