A recursive expectation-maximization algorithm for online multi-microphone noise reduction

Speech signals, captured by a microphone array mounted to a smart loudspeaker device, can be contaminated by ambient noise. In this paper, we present an online multichannel algorithm, based on the recursive EM (REM) procedure, to suppress ambient noise and enhance the speech signal. In the E-step of the proposed algorithm, a multichannel Wiener filter (MCWF) is applied to enhance the speech signal. The MCWF parameters, that is, the power spectral density (PSD) of the anechoic speech, the steering vector, and the PSD matrix of the noise, are estimated in the M-step. The proposed algorithm is specifically suitable for online applications since it uses only past and current observations and requires no iterations. To evaluate the proposed algorithm we used two sets of measurements. In the first set, static scenarios were generated by convolving speech utterances with real room impulse responses (RIRs) recorded in our acoustic lab with reverberation time set to 0.16 s and several signal to directional noise ratio (SDNR) levels. The second set was used to evaluate dynamic scenarios by using real recordings acquired by CEVA “smart and connected” development platform. Two practical use cases were evaluated: 1) estimating the steering vector with a known noise PSD matrix and 2) estimating the noise PSD matrix with a known steering vector. In both use cases, the proposed algorithm outperforms baseline multichannel denoising algorithms.