1,602 research outputs found
Non-linear echo cancellation - a Bayesian approach
Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel
Spatial-temporal Graph Based Multi-channel Speaker Verification With Ad-hoc Microphone Arrays
The performance of speaker verification degrades significantly in adverse
acoustic environments with strong reverberation and noise. To address this
issue, this paper proposes a spatial-temporal graph convolutional network (GCN)
method for the multi-channel speaker verification with ad-hoc microphone
arrays. It includes a feature aggregation block and a channel selection block,
both of which are built on graphs. The feature aggregation block fuses speaker
features among different time and channels by a spatial-temporal GCN. The
graph-based channel selection block discards the noisy channels that may
contribute negatively to the system. The proposed method is flexible in
incorporating various kinds of graphs and prior knowledge. We compared the
proposed method with six representative methods in both real-world and
simulated environments.
Experimental results show that the proposed method achieves a relative equal
error rate (EER) reduction of lower than the strongest
referenced method in the simulated datasets, and lower than
the latter in the real datasets. Moreover, its performance is robust across
different signal-to-noise ratios and reverberation time
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees
This paper addresses the problem of ad hoc microphone array calibration where
only partial information about the distances between microphones is available.
We construct a matrix consisting of the pairwise distances and propose to
estimate the missing entries based on a novel Euclidean distance matrix
completion algorithm by alternative low-rank matrix completion and projection
onto the Euclidean distance space. This approach confines the recovered matrix
to the EDM cone at each iteration of the matrix completion algorithm. The
theoretical guarantees of the calibration performance are obtained considering
the random and locally structured missing entries as well as the measurement
noise on the known distances. This study elucidates the links between the
calibration error and the number of microphones along with the noise level and
the ratio of missing distances. Thorough experiments on real data recordings
and simulated setups are conducted to demonstrate these theoretical insights. A
significant improvement is achieved by the proposed Euclidean distance matrix
completion algorithm over the state-of-the-art techniques for ad hoc microphone
array calibration.Comment: In Press, available online, August 1, 2014.
http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal
Processing, 201
A robust sequential hypothesis testing method for brake squeal localisation
This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)
Seeing the sound: a new multimodal imaging device for computer vision
Audio imaging can play a fundamental role in computer vision, in particular in automated surveillance, boosting the accuracy of current systems based on standard optical cameras. We present here a new hybrid device for acousticoptic imaging, whose characteristics are tailored to automated surveillance. In particular, the device allows realtime, high frame rate generation of an acoustic map, overlaid over a standard optical image using a geometric calibration of audio and video streams. We demonstrate the potentialities of the device for target tracking on three challenging setup showing the advantages of using acoustic images against baseline algorithms on image tracking. In particular, the proposed approach is able to overcome, often dramatically, visual tracking with state-of-art algorithms, dealing efficiently with occlusions, abrupt variations in visual appearence and camouflage. These results pave the
way to a widespread use of acoustic imaging in application scenarios such as in surveillance and security
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