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

TL;DR

X-ARES is a comprehensive, open-source benchmark suite that systematically evaluates audio encoder performance across multiple domains and tasks, revealing significant variability in state-of-the-art models.

Contribution

The paper introduces X-ARES, a novel framework with 22 diverse tasks and dual evaluation methods, advancing standardized assessment of audio representations.

Findings

Performance varies significantly across tasks and models

X-ARES covers speech, environmental sounds, and music domains

Highlights the complexity of general audio representation learning

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.