140 research outputs found
Post-Processing Independent Evaluation of Sound Event Detection Systems
Due to the high variation in the application requirements of sound event
detection (SED) systems, it is not sufficient to evaluate systems only in a
single operating mode. Therefore, the community recently adopted the polyphonic
sound detection score (PSDS) as an evaluation metric, which is the normalized
area under the PSD receiver operating characteristic (PSD-ROC). It summarizes
the system performance over a range of operating modes resulting from varying
the decision threshold that is used to translate the system output scores into
a binary detection output. Hence, it provides a more complete picture of the
overall system behavior and is less biased by specific threshold tuning.
However, besides the decision threshold there is also the post-processing that
can be changed to enter another operating mode. In this paper we propose the
post-processing independent PSDS (piPSDS) as a generalization of the PSDS.
Here, the post-processing independent PSD-ROC includes operating points from
varying post-processings with varying decision thresholds. Thus, it summarizes
even more operating modes of an SED system and allows for system comparison
without the need of implementing a post-processing and without a bias due to
different post-processings. While piPSDS can in principle combine different
types of post-processing, we hear, as a first step, present median filter
independent PSDS (miPSDS) results for this year's DCASE Challenge Task4a
systems. Source code is publicly available in our sed_scores_eval package
(https://github.com/fgnt/sed_scores_eval).Comment: submitted to DCASE Workshop 202
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
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