X-ARES: A Comprehensive Framework for Assessing Audio Encoder Performance
Abstract
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.
Keywords
Cite
@article{arxiv.2505.16369,
title = {X-ARES: A Comprehensive Framework for Assessing Audio Encoder Performance},
author = {Junbo Zhang and Heinrich Dinkel and Yadong Niu and Chenyu Liu and Si Cheng and Anbei Zhao and Jian Luan},
journal= {arXiv preprint arXiv:2505.16369},
year = {2025}
}
Comments
Accepted by Interspeech 2025