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

arXiv: https://arxiv.org/abs/2505.16369 · HTML: https://mlsanity.com/paper/2505.16369/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2025.

## Built on by (most cited first, 8 of 8)

- [UniSRCodec: Unified and Low-Bitrate Single Codebook Codec with Sub-Band Reconstruction](https://mlsanity.com/paper/2601.02776/) (2026)
- [UniWhisper: Efficient Continual Multi-task Training for Robust Universal Audio Representation](https://mlsanity.com/paper/2602.21772/) (2026)
- [AU-Harness: An Open-Source Toolkit for Holistic Evaluation of Audio LLMs](https://mlsanity.com/paper/2509.08031/) (2025)
- [LoSATok: Low-Dimensional Semantic-Acoustic Tokenizer for Cross-Domain Audio Understanding and Generation](https://mlsanity.com/paper/2605.27840/) (2026)
- [BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations](https://mlsanity.com/paper/2606.30700/) (2026)
- [Discrete vs. Continuous: A Comprehensive Study of Unified Audio Understanding in LALMs](https://mlsanity.com/paper/2609.22851/) (2026)
- [UniAE-MoE: A Unified Audio Encoder via Mixture of Experts](https://mlsanity.com/paper/2609.39199/) (2026)
- [RepICL: Reusable In-Context Prediction Across Heterogeneous Representation Spaces](https://mlsanity.com/paper/2610.05852/) (2026)

## 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.