Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model
EURASIP Journal on Applied Signal Processing
Handbook on Array Processing and Sensor Networks
Handbook on Array Processing and Sensor Networks
IEEE Transactions on Signal Processing
GSVD-based optimal filtering for single and multimicrophone speech enhancement
IEEE Transactions on Signal Processing
Signal enhancement using beamforming and nonstationarity withapplications to speech
IEEE Transactions on Signal Processing
Clustered Blind Beamforming From Ad-Hoc Microphone Arrays
IEEE Transactions on Audio, Speech, and Language Processing
Reduced-Bandwidth and Distributed MWF-Based Noise Reduction Algorithms for Binaural Hearing Aids
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Information Theory
IEEE Transactions on Signal Processing
Distributed Node-Specific LCMV Beamforming in Wireless Sensor Networks
IEEE Transactions on Signal Processing
An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech
IEEE Transactions on Audio, Speech, and Language Processing
Spectral Magnitude Minimum Mean-Square Error Estimation Using Binary and Continuous Gain Functions
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
In this paper, we investigate the use of randomized gossip for distributed speech enhancement and present a distributed delay and sum beamformer (DDSB). In a randomly connected wireless acoustic sensor network, the DDSB estimates the desired signal at each node by communicating only with its neighbors. We first provide the asynchronous DDSB (ADDSB) where each pair of neighboring nodes updates its data asynchronously. Then, we introduce an improved general distributed synchronous averaging (IGDSA) algorithm, which can be used in any connected network, and combine that with the DDSB algorithm where multiple node pairs can update their estimates simultaneously. For convergence analysis, we first provide bounds for the worst case averaging time of the ADDSB for the best and worst connected networks, and then we compare the convergence rate of the ADDSB with the original synchronous DDSB (OSDDSB) and the improved synchronous DDSB (ISDDSB) in regular networks. This convergence rate comparison is extended to randomly connected non-regular networks using simulations. The simulation results show that the DDSB using the different updating schemes converges to the optimal estimates of the centralized beamformer and that the proposed IGDSA algorithm converges much faster than the original synchronous communication scheme, in particular for non-regular networks. Moreover, comparisons are performed with several existing distributed speech enhancement methods from literature, assuming that the steering vector is given. In the simulated scenario, the proposed method leads to a slight performance improvement at the expense of a higher communication cost. The presented method is not constrained to a certain network topology (e.g., tree connected or fully connected), while this is the case for many of the reference methods.