X-ARES: A Comprehensive Framework for Assessing Audio Encoder Performance
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Abstract (arXiv)
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.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 May 2025.
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Citing papers per year: 2025: 1; 2026: 7.
8 papers.
- 2026UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstructioncited by 2
- 2026UniWhisper: Efficient Continual Multi-task Training for Robust Universal Audio Representationcited by 1
- 2025AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMscited by 0
- 2026LoSATok: Low-Dimensional Semantic-Acoustic Tokenizer for Cross-Domain Audio Understanding and Generationcited by 0
- 2026BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representationscited by 0
- 2026Discrete vs. Continuous: A Comprehensive Study of Unified Audio Understanding in LALMscited by 0
- 2026UniAE-MoE: A Unified Audio Encoder via Mixture of Expertscited by 0
- 2026RepICL: Reusable In-Context Prediction Across Heterogeneous Representation Spacescited by 0
- 2026RepICL: Reusable In-Context Prediction Across Heterogeneous Representation Spaces5 Oct 2026
- 2026UniAE-MoE: A Unified Audio Encoder via Mixture of Experts30 Sep 2026
- 2026Discrete vs. Continuous: A Comprehensive Study of Unified Audio Understanding in LALMs19 Sep 2026
- 2026BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations29 Jun 2026
- 2026LoSATok: Low-Dimensional Semantic-Acoustic Tokenizer for Cross-Domain Audio Understanding and Generation27 May 2026
- 2026UniWhisper: Efficient Continual Multi-task Training for Robust Universal Audio Representation25 Feb 2026
- 2026UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction6 Jan 2026
- 2025AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs9 Sep 2025