13 accepted papers
Sample-accurate synchronization of nodes is required to enable the full potential of acoustic sensor networks for cooperative and enhanced signal acquisition. While metrics of spatio-temporal sensor utility are key to successful aggregation of sensor nodes, for instance, to perform sound localizatio…
System identification is instrumental in various tasks of acoustics, including acoustic measurement and acoustic echo cancellation. Adaptive filtering is proven to be successful for the identification of linear parts with variable impulse responses. Neural network frameworks are recently considered…
Distributed hardware of acoustic sensor networks bears inconsistency of local sampling frequencies, which is detrimental to signal processing. Fundamentally, sampling rate offset (SRO) nonlinearly relates the discrete-time signals acquired by different sensor nodes. As such, retrieval of SRO from th…
Binaural speech enhancement has often suffered from the trade-off between noise reduction and spatial cue preservation. The common-gain filtering of noisy speech under minimum mean-square error (MMSE) turned out as a viable approach, which resembles the format of Wiener-filtering spectral enhancemen…
While speech enhancement has critically required the estimation of local time-varying SNR, it was recently shown that SNR can be marginalized in a Bayesian sense from the minimum-mean-square-error (MMSE) solution. Precisely, the local SNR is introduced as a stochastic variable and Bayesian integrati…
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 yo…
Fast and comprehensive acquisition of head-related impulse responses (HRIRs) continues in the interest of rich application scenarios. Various HRIR resolutions have been presented based on discrete stop-and-go measurement, or comprehensive measurement equipment, or continuous-azimuth acquisition with…
This paper is devoted to high-fidelity implementation of HRTF-based binaural rendering with fast head and source rotations in virtual acoustic reality. With an intuitive physical standpoint, we argue that head rotations should be rendered by a convolution model anchored in the sound receive-time. Co…
We demonstrate the feasibility of the realtime implementation of advanced binaural noise reduction algorithms in a single-chip computer called Raspberry Pi. The implementation of the considered algorithms is realized in Simulink, a graphical programming add-on to the integrated development environme…
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 approa…
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…
In this paper we propose a novel approach to cepstral smoothing for reducing musical noise fluctuations in binaural speech enhancement. Similar to other methods, our approach computes a preliminary spectral gain function using the magnitude-squared coherence function and applies an instantaneous wei…
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 wa…