Relative Transfer Function Estimation Exploiting Spatially Separated Microphones in a Diffuse Noise Field
This work addresses speech enhancement for multi-microphone systems in noisy environments, representing an incremental improvement over existing methods.
The paper tackled the problem of estimating the relative transfer function (RTF) vector for speech enhancement in diffuse noise fields by proposing a method that uses an additional spatially separated microphone to reduce noise coherence, resulting in improved estimation accuracy and noise reduction performance compared to state-of-the-art estimators.
Many multi-microphone speech enhancement algorithms require the relative transfer function (RTF) vector of the desired speech source, relating the acoustic transfer functions of all array microphones to a reference microphone. In this paper, we propose a computationally efficient method to estimate the RTF vector in a diffuse noise field, which requires an additional microphone that is spatially separated from the microphone array, such that the spatial coherence between the noise components in the microphone array signals and the additional microphone signal is low. Assuming this spatial coherence to be zero, we show that an unbiased estimate of the RTF vector can be obtained. Based on real-world recordings experimental results show that the proposed RTF estimator outperforms state-of-the-art estimators using only the microphone array signals in terms of estimation accuracy and noise reduction performance.