Joint Maximum Likelihood Estimation of Microphone Array Parameters for a Reverberant Single Source Scenario

Abstract: Estimation of the acoustic-scene related parameters such as relative transfer functions (RTFs) from source to microphones, source power spectral densities (PSDs) and PSDs of the late reverberation is essential and also challenging. Existing maximum likelihood estimators typically consider only subsets of these parameters and use each time frame separately. In this paper we explicitly focus on the single source scenario and first propose a joint maximum likelihood estimator (MLE) to estimate all parameters jointly using a single time frame. Since the RTFs are typically invariant for a number of consecutive time frames we also propose a joint maximum likelihood estimator (MLE) using multiple time frames which has similar estimation performance compared to a recently proposed reference algorithm called simultaneously confirmatory factor analysis (SCFA), but at a much lower complexity. Moreover, we present experimental results which demonstrate that the estimation accuracy, together with the performance of noise reduction, speech quality and speech intelligibility, of our proposed joint MLE outperform those of existing MLE based approaches that use only a single time frame.

Related publications

1. Joint Maximum Likelihood Estimation of Microphone Array Parameters for a Reverberant Single Source Scenario
   Changheng Li; Jorge Martinez; Richard C. Hendriks;
   IEEE/ACM Trans. Audio Speech Lang. Process.,
   Volume 31, pp. 695–705, 2023. DOI: 10.1109/TASLP.2022.3231706
   document