Coherent processing of signals captured by a wireless acoustic sensor network (WASN) requires an estimation of unknown parameters such as the sampling-rate and sampling-time offset (SRO and STO) of asynchronous sensor clocks. Although some sophisticated techniques for blind parameter estimation have become available in this young field of research, further development of new methods is required especially regarding environmental robustness, online estimation, and computational efficiency. As the main contribution of this work, we therefore introduce a novel approach for blind SRO estimation in the spirit of both a recently available time-domain double-cross-correlation processor (DXCP) and the well-known generalized cross-correlation with phase transform (GCC-PhaT). For the proposed approach, called DXCP-PhaT, we specifically introduce secondary cross-quantities to restore ergodicity of the otherwise drifting cross-correlation function or cross-spectrum of asynchronous input signals, which is an important property for estimation. Based upon this theoretical contribution of the paper, we then derive the DXCP-PhaT algorithm in the STFT domain, which brings along a number of improvements over state-of-the-art: advanced accuracy in terms of SRO assessment, environmental robustness to larger microphone distances as of real sensor networks, realtime-applicability in terms of online architecture and reduced complexity, and eventually an extension with online STO estimation (up to the inherent ambiguity of digital STO and acoustic TDOA) reliant on SRO compensation. Those claims are confirmed by comprehensive experiments, both offline and online, on simulated data and real recordings from a Rasberry-Pi-based WASN.

Signal synchronization in wireless acoustic sensor networks requires an accurate estimation of the sampling rate offset (SRO) inevitably present in signals acquired by sensors of ad-hoc networks. Although some sophisticated methods for blind SRO estimation have been recently proposed in this very young field of research, there is still a need for the development of new ideas and concepts especially regarding robust approaches with low computational complexity. We therefore propose a novel time-domain method based on the calculation of a double-cross-correlation function in this contribution. Experimental evaluation of the introduced approach in a challenging acoustic environment and comparison with a state-of-the-art frequency-domain method confirms the high accuracy and low computational load of the proposed technique.

MMSE filtering of speech with additive noise and latent speech power-spectral density (PSD) is addressed. This problem is strong in single-channel speech enhancement and restricts the utility of stationary Wiener filters or other statistical estimators based on PSDs. The issue typically manifests itself in residual noise after filtering, despite the availability of the noise PSD. Our paper therefore incorporates the latent speech PSD state via marginalization into the MMSE estimation framework of complex speech spectral amplitudes. The hence involved joint posterior distribution of the complex speech amplitude and speech PSD, conditioned on just the noisy observations, is then resolved in the Bayesian sense into a speech and a speech-PSD posterior. The latter is expressed via the local data likelihood and a hyper-prior of the local speech PSD or a-priori SNR-i.e., a global distribution across the entire speech signal. Marginalization, in this way, turns into expectation over a latent Wiener filter, such that explicit estimation of local a-priori SNR is eliminated. The local input data in the form of the a-posteriori SNR and the global SNR value as a descriptor of the overall speech-in-noise condition turns out sufficient to control our resulting MMSE spectral gain function, and, potentially, can be provided much easier than the latent and time-varying a-priori SNR. An improved balance of residual noise and speech quality in the enhancement of noisy speech is demonstrated by objective experimental evaluation.

MMSE filtering of signals contaminated with additive noise is addressed with explicit uncertainty of the second-order target signal statistics. The unfortunate lack of stationarity of speech, and hence the phenomenon of musical noise in speech enhancement, is an ideal problem for the proposed approach. Specifically, we complement the established short-time power-spectral subtraction for speech power estimation with a prior of the momentary speech-power level. The MMSE estimator for Gaussian speech amplitudes is then derived under these circumstances. The potential for the enhancement of noisy speech is briefly demonstrated by SNR and PESQ analysis.

In linear system identification, the coexistence of parameter-misadjustment and output-error metrics has turned out very practical and their relation is well understood. In nonlinear system identification, however, such tools for performance evaluation are far less developed and each nonlinear type may need its own treatment. This paper focuses on the Hammerstein model as an instance of nonlinear systems. Irrespective of particular identification algorithms, we generalize the framework of parameter-and output-based performance metrics known from linear systems. An ambiguity in system parameters is resolved via the projection misalignment technique.

Extracting a target source signal from multiple noisy observations is an essential task in many applications of signal processing such as digital communications or speech and audio processing. The multi-channel Wiener filter is able to solve this task in a minimum-mean-square-error (MMSE) optimal way by applying a spatial filter succeeded by a spectral postfilter. Its direct implementation, however, is difficult due to requiring the statistics of the unobservable source and noise signals. In this paper, we apply the signal-separation-based technique of multichannel decorrelation and reveal its relation to the Wiener post-filtering component. On this basis, we present a numerically robust and efficient adaptive algorithm to find an estimate of the MMSE-optimal postfilter based on the statistics of the observable signals alone. Experimental evaluation demonstrates the validity of the proposed approach and confirms the convergence of the adaptive algorithm to the MMSE-optimal postfilter solution.

Creating an immersive listening experience and providing the audience with improved spatial realism is the goal of many adaptive audio reproduction techniques such as room equalization or crosstalk cancellation. The majority of these approaches currently relies on acoustic impulse responses (AIRs) that have been measured prior to the actual audio reproduction. In order to maintain a high degree of adaptivity, however, the AIRs need to be estimated online during the reproduction process, which turns out to be a severely ill-conditioned problem due to the high inter-channel correlation of the loudspeaker signals. In this paper, we present a novel approach to MISO system identification with realistically correlated excitation. Based on the idea of separate treatment of correlated and uncorrelated signal components, we propose two extended filter structures for gradient-descent-based adaptive system identification and provide theoretical analysis and experimental validation of their effectiveness.