2,315 research outputs found
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
LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices
We present LibriWASN, a data set whose design follows closely the LibriCSS
meeting recognition data set, with the marked difference that the data is
recorded with devices that are randomly positioned on a meeting table and whose
sampling clocks are not synchronized. Nine different devices, five smartphones
with a single recording channel and four microphone arrays, are used to record
a total of 29 channels. Other than that, the data set follows closely the
LibriCSS design: the same LibriSpeech sentences are played back from eight
loudspeakers arranged around a meeting table and the data is organized in
subsets with different percentages of speech overlap. LibriWASN is meant as a
test set for clock synchronization algorithms, meeting separation, diarization
and transcription systems on ad-hoc wireless acoustic sensor networks. Due to
its similarity to LibriCSS, meeting transcription systems developed for the
former can readily be tested on LibriWASN. The data set is recorded in two
different rooms and is complemented with ground-truth diarization information
of who speaks when.Comment: Accepted for presentation at the ITG conference on Speech
Communication 202
Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks
We propose a diarization system, that estimates "who spoke when" based on
spatial information, to be used as a front-end of a meeting transcription
system running on the signals gathered from an acoustic sensor network (ASN).
Although the spatial distribution of the microphones is advantageous,
exploiting the spatial diversity for diarization and signal enhancement is
challenging, because the microphones' positions are typically unknown, and the
recorded signals are initially unsynchronized in general. Here, we approach
these issues by first blindly synchronizing the signals and then estimating
time differences of arrival (TDOAs). The TDOA information is exploited to
estimate the speakers' activity, even in the presence of multiple speakers
being simultaneously active. This speaker activity information serves as a
guide for a spatial mixture model, on which basis the individual speaker's
signals are extracted via beamforming. Finally, the extracted signals are
forwarded to a speech recognizer. Additionally, a novel initialization scheme
for spatial mixture models based on the TDOA estimates is proposed. Experiments
conducted on real recordings from the LibriWASN data set have shown that our
proposed system is advantageous compared to a system using a spatial mixture
model, which does not make use of external diarization information.Comment: Accepted at Asilomar Conference on Signals, Systems, and Computers
202
Sample Drop Detection for Distant-speech Recognition with Asynchronous Devices Distributed in Space
In many applications of multi-microphone multi-device processing, the
synchronization among different input channels can be affected by the lack of a
common clock and isolated drops of samples. In this work, we address the issue
of sample drop detection in the context of a conversational speech scenario,
recorded by a set of microphones distributed in space. The goal is to design a
neural-based model that given a short window in the time domain, detects
whether one or more devices have been subjected to a sample drop event. The
candidate time windows are selected from a set of large time intervals,
possibly including a sample drop, and by using a preprocessing step. The latter
is based on the application of normalized cross-correlation between signals
acquired by different devices. The architecture of the neural network relies on
a CNN-LSTM encoder, followed by multi-head attention. The experiments are
conducted using both artificial and real data. Our proposed approach obtained
F1 score of 88% on an evaluation set extracted from the CHiME-5 corpus. A
comparable performance was found in a larger set of experiments conducted on a
set of multi-channel artificial scenes.Comment: Submitted to ICASSP 202
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