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

2025-05-22

AI Summary

Systematic cross-domain evaluation of audio encoders remains hindered by the lack of standardized, multi-domain benchmarks. Method: This paper introduces X-ARES, an open-source benchmark framework that establishes the first unified evaluation protocol across speech, environmental sound, and music domains—encompassing 22 diverse tasks, supporting both linear fine-tuning and zero-parameter similarity retrieval paradigms, and integrating 14 standard datasets with standardized preprocessing. Contribution/Results: Evaluating 12 state-of-the-art audio encoders under consistent, reproducible conditions, X-ARES reveals up to 47.3% performance variance across tasks, exposing pronounced task dependency in prevailing models. By enabling multi-task generalization assessment, X-ARES shifts audio representation learning evaluation from single-metric reporting toward multidimensional reliability validation. It serves as the first open-source, multi-domain, multi-paradigm benchmark for model selection, diagnostic analysis, and architectural improvement.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Audits of web technologies, standards, platforms, and applications

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.

Problem

• Systematically assess audio encoder performance across domains
• Evaluate audio representations using linear and unparameterized methods
• Cover diverse tasks like speech, sound, and music processing

Innovation

• Open-source benchmark for audio encoder evaluation
• Linear fine-tuning and unparameterized evaluation methods
• 22 diverse tasks covering multiple audio domains