Performance and robustness of signal-dependent vs. signal-independent binaural signal matching with wearable microphone arrays

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 for high DRR and with a rotation of the listener’s head, while showing robustness to errors in the estimation of the source direction. Under low DRR conditions, performance of the signal-dependent methods become comparable to BSM, while still providing improvement with listener’s head rotation.