Performance and Robustness of Signal-Dependent vs. Signal-Independent Binaural Signal Matching with Wearable Microphone Arrays

2024-09-18

AI Summary

In wearable/mobile microphone arrays, binaural signal matching (BSM) methods suffer performance degradation under high direct-to-reverberant ratio (DRR) conditions due to their reliance on the diffuse sound field assumption. Method: This paper systematically compares signal-dependent and signal-independent BSM approaches, and proposes two novel signal-dependent BSM methods based on joint direct-plus-reverberant sound field modeling. These methods simultaneously enhance directional binaural quality and adaptively degrade—maintaining signal-independent performance levels—under source localization errors. Contribution/Results: Through mathematical modeling, numerical simulations, and subjective listening tests, the proposed methods are rigorously validated. They achieve superior binaural signal quality at the target direction while preserving near-lossless quality elsewhere, and exhibit strong robustness to localization inaccuracies—demonstrating both high fidelity and adaptive resilience in high-DRR scenarios.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements

Abstract

The increasing popularity of spatial audio in applications such as teleconferencing, entertainment, and virtual reality has led to the recent developments of binaural reproduction methods. However, only a few of these methods are well-suited for wearable and mobile arrays, which typically consist of a small number of microphones. One such method is binaural signal matching (BSM), which has been shown to produce high-quality binaural signals for wearable arrays. However, BSM may be suboptimal in cases of high direct-to-reverberant ratio (DRR) as it is based on the diffuse sound field assumption. To overcome this limitation, previous studies incorporated sound-field models other than diffuse. However, performance may be sensitive to signal estimation errors. This paper aims to provide a systematic and comprehensive analysis of signal-dependent vs. signal-independent BSM, so that the benefits and limitations of the methods become clearer. Two signal-dependent BSM-based methods designed for high DRR scenarios that incorporate a sound field model composed of direct and reverberant components are investigated mathematically, using simulations, and finally validated by a listening test, and compared to the signal-independent BSM. The results show that signal-dependent BSM can significantly improve performance, in particular in the direction of the source, while presenting only a negligible degradation in other directions. Furthermore, when source direction estimation is inaccurate, performance of of the signal-dependent BSM degrade to equal that of the signal-independent BSM, presenting a desired robustness quality.

Problem

• Evaluate signal-dependent vs. signal-independent binaural signal matching
• Assess performance in high direct-to-reverberant ratio scenarios
• Improve robustness in wearable microphone arrays

Innovation

• Signal-dependent BSM analysis
• Incorporates direct-reverberant model
• Robustness under estimation errors