The ICME 2025 Audio Encoder Capability Challenge

2025-01-25

AI Summary

This study addresses the insufficient evaluation of audio encoders’ cross-task and cross-scenario generalization capabilities by proposing the first standardized benchmark framework designed for real-world usability. Methodologically, it introduces a dual-track evaluation protocol: Track A (parameterized) assesses fine-tuning performance of pretrained encoders on diverse downstream tasks—including speech, environmental sound, and music—while Track B (parameter-free) evaluates embedding quality under zero-shot and few-shot transfer settings. The framework operates directly on raw waveforms and is compatible with self-supervised learning, contrastive learning, and multi-task adaptation paradigms. Its primary contributions are threefold: (i) overcoming the limitations of single-task benchmarks; (ii) enabling the first systematic quantification of audio encoders’ robustness, parameter efficiency, and cross-domain transferability; and (iii) establishing a new paradigm and open-source benchmark for developing and evaluating efficient, general-purpose audio representation models.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Sampling/Simulation-based Search

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

This challenge aims to evaluate the capabilities of audio encoders, especially in the context of multi-task learning and real-world applications. Participants are invited to submit pre-trained audio encoders that map raw waveforms to continuous embeddings. These encoders will be tested across diverse tasks including speech, environmental sounds, and music, with a focus on real-world usability. The challenge features two tracks: Track A for parameterized evaluation, and Track B for parameter-free evaluation. This challenge provides a platform for evaluating and advancing the state-of-the-art in audio encoder design.

Problem

• Audio Encoder Evaluation
• Performance Comparison
• Real-World Scenarios

Innovation

• Audio Encoder Competition
• Performance Evaluation
• Comprehensive Sound Testing