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

Junbo Zhang, Heinrich Dinkel, Yadong Niu, Chenyu Liu, Si Cheng, Anbei Zhao, Jian Luan

TL;DR

X-ARES introduces a comprehensive, open-source benchmark for evaluating audio encoders across speech, environmental sounds, and music. It provides two evaluation modalities—parameterized MLP fine-tuning and unparameterized k-NN—across 22 diverse tasks, enabling a holistic view of encoder capabilities. The framework integrates TaskConfig, WebDataset-based data loading, and fixed task components to streamline evaluation and reproducibility. Experiments show significant performance variation across encoders and domains, underscoring the need for cross-domain benchmarks to guide robust audio representation learning and deployment.

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.

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

TL;DR

X-ARES introduces a comprehensive, open-source benchmark for evaluating audio encoders across speech, environmental sounds, and music. It provides two evaluation modalities—parameterized MLP fine-tuning and unparameterized k-NN—across 22 diverse tasks, enabling a holistic view of encoder capabilities. The framework integrates TaskConfig, WebDataset-based data loading, and fixed task components to streamline evaluation and reproducibility. Experiments show significant performance variation across encoders and domains, underscoring the need for cross-domain benchmarks to guide robust audio representation learning and deployment.

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.

Paper Structure

This paper contains 18 sections, 2 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. HEAR: Holistic Evaluation of Audio Representations
  4. SUPERB: Speech processing Universal PERformance Benchmark
  5. DASB: Discrete Audio and Speech Benchmark
  6. Framework Design
  7. Overall Architecture
  8. Task Configuration and Data Processing
  9. Task-Specific Components
  10. User-Provided Audio Encoder
  11. Task Categories
  12. Evaluation Metrics
  13. Task-Specific Metrics
  14. Metric Normalization
  15. Experiments and Results
  16. ...and 3 more sections

Figures (3)

  • Figure 1: The proposed X-ARES framework. Users provide a single pretrained audioencoder, which outputs frame-level embeddings. Embeddings are evaluated using a fine-tuned MLP layer for clip- and frame-level tasks. Further a non-parameterized kNN algorithm is used to evaluate the quality of embeddings. For specialized tasks, pre-trained decoders are incorporated as task-specific components.
  • Figure 2: MLP evaluation results for each model and task, where higher is better.
  • Figure 3: k-NN evaluation results for each model and task, where higher is better.