Sampling rate synchronization is an inevitable issue in distributed acoustic sensor networks. In this paper, an analytical sampling rate offset (SRO) estimation approach is first proposed, and then, it is extended to a distributed method that suitable for acoustic sensor networks with arbitrary communication graphs. Specifically, a linear-phase drift model in the short-time Fourier transform domain is used to approximate the SRO between each pair of microphone nodes. Next, after unwrapping the temporally averaged phase information, SROs are recovered analytically via a new weighted-sum criterion. Based on this, a distributed cost function is established at each node to obtain the SROs of all nodes simultaneously in a distributed manner. Finally, a state-of-the-art distributed algorithm named asynchronous network Newton optimization is adopted to carry out the distributed SRO estimation. The proposed method can effectively estimate the SROs among acoustic sensor nodes in noisy and reverberant environments. Compared with the existing approaches, it does not require an external central processor, and only local communications among nodes are needed. Experimental results confirm the validity of the proposed method.

The sampling rate mismatches among nodes severely degrade the performances of distributed signal processing methods in acoustic sensor networks. An active sampling rate calibration method based on the multiple signal classification (MUSIC) algorithm is proposed to solve this problem. Specifically, a pre-defined sinusoidal signal is used as the calibration sound source, and the sampling rate mismatch problem is formulated based on the Hadamard product between output signals of pairwise nodes. The Hadamard product is then filtered through the pre-designed filters to obtain the target sinusoidal signals whose frequencies linearly correlate with the sampling rate mismatch. Finally, the sampling rate differences among nodes are estimated by the root-MUSIC algorithm and compensated by the sinc-interpolation. The proposed method can effectively calibrate the sampling rate mismatch even under severe noise and reverberation. Moreover, it has a strong tolerance for deviations among the microphone frequency responses. Experimental results reveal the validity of the proposed method.

Acoustic sensor networks (ASNs) are widely applied in scenarios like teleconference, teaching, and theatre. ASNs can be used in tracking speakers, enhancing the speaker's speech and human-machine interactions, etc., but the geometric structure of the ASN has to be calibrated. ASN geometry calibration is a challenging task due to the irregular geometric structures of ASNs. A three-dimensional (3D) node geometry calibration approach based on direction of arrival (DOA) measurements and artificial bee colony (ABC) algorithm is proposed in this paper. The theoretical DOAs of sound sources relative to nodes are first derived based on 3D rotation matrices and translation vectors, and the corresponding measured DOAs are estimated by the time-difference-of-arrival. Then, the node geometry calibration problem is formulated as the minimization of a cost function measuring the mismatch between theoretical and measured DOAs, and such non-convex minimization is effectively solved by the ABC algorithm. Next, Cramér-Rao bound is presented to provide a theoretical lower bound for DOA-based node geometry calibration. Finally, the sensitivity of the proposed method to the sound source position error is discussed. The proposed method can calibrate node geometry positions successfully in both 2D plane and 3D space and requires no information transmission among nodes when the positions of few sound sources and the relative geometry of microphones in each node are known. Experimental results reveal the validity of the proposed node geometry calibration method.