11,059 research outputs found
End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models
Speech activity detection (SAD) plays an important role in current speech
processing systems, including automatic speech recognition (ASR). SAD is
particularly difficult in environments with acoustic noise. A practical
solution is to incorporate visual information, increasing the robustness of the
SAD approach. An audiovisual system has the advantage of being robust to
different speech modes (e.g., whisper speech) or background noise. Recent
advances in audiovisual speech processing using deep learning have opened
opportunities to capture in a principled way the temporal relationships between
acoustic and visual features. This study explores this idea proposing a
\emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach
models the temporal dynamic of the sequential audiovisual data, improving the
accuracy and robustness of the proposed SAD system. Instead of estimating
hand-crafted features, the study investigates an end-to-end training approach,
where acoustic and visual features are directly learned from the raw data
during training. The experimental evaluation considers a large audiovisual
corpus with over 60.8 hours of recordings, collected from 105 speakers. The
results demonstrate that the proposed framework leads to absolute improvements
up to 1.2% under practical scenarios over a VAD baseline using only audio
implemented with deep neural network (DNN). The proposed approach achieves
92.7% F1-score when it is evaluated using the sensors from a portable tablet
under noisy acoustic environment, which is only 1.0% lower than the performance
obtained under ideal conditions (e.g., clean speech obtained with a high
definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio
Detect to Track and Track to Detect
Recent approaches for high accuracy detection and tracking of object
categories in video consist of complex multistage solutions that become more
cumbersome each year. In this paper we propose a ConvNet architecture that
jointly performs detection and tracking, solving the task in a simple and
effective way. Our contributions are threefold: (i) we set up a ConvNet
architecture for simultaneous detection and tracking, using a multi-task
objective for frame-based object detection and across-frame track regression;
(ii) we introduce correlation features that represent object co-occurrences
across time to aid the ConvNet during tracking; and (iii) we link the frame
level detections based on our across-frame tracklets to produce high accuracy
detections at the video level. Our ConvNet architecture for spatiotemporal
object detection is evaluated on the large-scale ImageNet VID dataset where it
achieves state-of-the-art results. Our approach provides better single model
performance than the winning method of the last ImageNet challenge while being
conceptually much simpler. Finally, we show that by increasing the temporal
stride we can dramatically increase the tracker speed.Comment: ICCV 2017. Code and models:
https://github.com/feichtenhofer/Detect-Track Results:
https://www.robots.ox.ac.uk/~vgg/research/detect-track
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