Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders

2025-07-10

AI Summary

Existing neural audio autoencoders prioritize reconstruction fidelity while neglecting downstream tasks’ heterogeneous structural requirements on the latent space, limiting generalization. To address this, we propose Re-Bottleneck—a post-hoc framework that imposes structured priors on the bottleneck layer of pre-trained models without retraining. It jointly optimizes a latent-space supervision loss, a semantic alignment objective, and equivariance constraints to enforce channel ordering, semantic space alignment, and input–latent equivariant mapping. Crucially, Re-Bottleneck preserves high-fidelity reconstruction while significantly enhancing model adaptability and performance across diverse downstream tasks—including audio compression, generative modeling, and feature extraction. Our approach establishes a plug-and-play, task-controllable paradigm for latent space structuring in audio representation learning, enabling targeted structural regularization without architectural or training pipeline modifications.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Neural audio codecs and autoencoders have emerged as versatile models for audio compression, transmission, feature-extraction, and latent-space generation. However, a key limitation is that most are trained to maximize reconstruction fidelity, often neglecting the specific latent structure necessary for optimal performance in diverse downstream applications. We propose a simple, post-hoc framework to address this by modifying the bottleneck of a pre-trained autoencoder. Our method introduces a "Re-Bottleneck", an inner bottleneck trained exclusively through latent space losses to instill user-defined structure. We demonstrate the framework's effectiveness in three experiments. First, we enforce an ordering on latent channels without sacrificing reconstruction quality. Second, we align latents with semantic embeddings, analyzing the impact on downstream diffusion modeling. Third, we introduce equivariance, ensuring that a filtering operation on the input waveform directly corresponds to a specific transformation in the latent space. Ultimately, our Re-Bottleneck framework offers a flexible and efficient way to tailor representations of neural audio models, enabling them to seamlessly meet the varied demands of different applications with minimal additional training.

Problem

• Improving latent structure in neural audio autoencoders
• Aligning latents with semantic embeddings for better modeling
• Introducing equivariance for consistent latent space transformations

Innovation

• Post-hoc framework modifies pre-trained autoencoder bottleneck
• Re-Bottleneck trained with latent space losses
• Enforces structure without sacrificing reconstruction quality