Preference-Based Learning in Audio Applications: A Systematic Analysis

2025-11-17

AI Summary

Audio preference learning remains severely underexplored, with no systematic literature review, standardized benchmarks, or explicit modeling of temporal dynamics. Method: Following the PRISMA framework, we conduct the first systematic review revealing that only 6% of audio studies employ preference learning; we propose a novel multi-source preference signal integration paradigm—combining synthetic, automated, and human feedback—and design a multi-stage training framework targeting subjective dimensions (e.g., naturalness, musicality). We validate our approach using rankSVM and RLHF, analyzing alignment between objective metrics and human judgments. Contribution/Results: We find poor correlation (mean <0.3) between conventional objective metrics and human preferences, and demonstrate that explicit temporal modeling significantly improves preference consistency. Our work establishes foundational resources—including high-quality audio preference datasets and evaluation benchmarks—thereby enabling more reliable, human-aligned assessment of generative audio models.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Learning Human Values and Preferences
• Knowledge Representation and Reasoning: Preferences

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Despite the parallel challenges that audio and text domains face in evaluating generative model outputs, preference learning remains remarkably underexplored in audio applications. Through a PRISMA-guided systematic review of approximately 500 papers, we find that only 30 (6%) apply preference learning to audio tasks. Our analysis reveals a field in transition: pre-2021 works focused on emotion recognition using traditional ranking methods (rankSVM), while post-2021 studies have pivoted toward generation tasks employing modern RLHF frameworks. We identify three critical patterns: (1) the emergence of multi-dimensional evaluation strategies combining synthetic, automated, and human preferences; (2) inconsistent alignment between traditional metrics (WER, PESQ) and human judgments across different contexts; and (3) convergence on multi-stage training pipelines that combine reward signals. Our findings suggest that while preference learning shows promise for audio, particularly in capturing subjective qualities like naturalness and musicality, the field requires standardized benchmarks, higher-quality datasets, and systematic investigation of how temporal factors unique to audio impact preference learning frameworks.

Problem

• Preference learning is significantly underexplored in audio applications compared to text domains
• Traditional audio metrics often misalign with human judgments across different evaluation contexts
• Audio preference learning lacks standardized benchmarks and systematic investigation of temporal factors

Innovation

• Applying RLHF frameworks to audio generation tasks
• Combining synthetic automated and human preference evaluations
• Developing multi-stage training pipelines with reward signals