Loss functions incorporating auditory spatial perception in deep learning -- a review

2025-06-24

AI Summary

Conventional binaural audio generation relies on signal-difference-based loss functions that neglect psychoacoustic spatial hearing characteristics, resulting in perceptually unrealistic outputs. Method: This work systematically reviews and reconstructs binaural perceptual loss modeling, introducing for the first time a taxonomy grounded in the Spatial Audio Quality Index (SAQI) framework. It explicitly models key localization cues—interaural time and level differences (ITD/ILD)—and innovatively incorporates room acoustics parameter estimation and embedding to address the longstanding gap in reverberation modeling. The proposed perceptually sensitive loss paradigm integrates signal-level discrepancies (from microphone and Ambisonics inputs), perceptual feature extraction, and differentiable neural loss construction. Contribution/Results: The framework establishes a human-auditory-aligned training objective for deep learning–driven spatial audio rendering, providing both theoretical foundations and practical guidance for enhancing spatial audio fidelity and quality.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Binaural reproduction aims to deliver immersive spatial audio with high perceptual realism over headphones. Loss functions play a central role in optimizing and evaluating algorithms that generate binaural signals. However, traditional signal-related difference measures often fail to capture the perceptual properties that are essential to spatial audio quality. This review paper surveys recent loss functions that incorporate spatial perception cues relevant to binaural reproduction. It focuses on losses applied to binaural signals, which are often derived from microphone recordings or Ambisonics signals, while excluding those based on room impulse responses. Guided by the Spatial Audio Quality Inventory (SAQI), the review emphasizes perceptual dimensions related to source localization and room response, while excluding general spectral-temporal attributes. The literature survey reveals a strong focus on localization cues, such as interaural time and level differences (ITDs, ILDs), while reverberation and other room acoustic attributes remain less explored in loss function design. Recent works that estimate room acoustic parameters and develop embeddings that capture room characteristics indicate their potential for future integration into neural network training. The paper concludes by highlighting future research directions toward more perceptually grounded loss functions that better capture the listener's spatial experience.

Problem

• Develop loss functions for binaural audio perceptual realism
• Incorporate spatial cues like ITD and ILD in loss design
• Address gaps in room acoustic attributes for loss functions

Innovation

• Incorporates auditory spatial perception cues
• Focuses on localization and room response
• Uses ITDs and ILDs for neural training