179 research outputs found
Reading Scene Text in Deep Convolutional Sequences
We develop a Deep-Text Recurrent Network (DTRN) that regards scene text
reading as a sequence labelling problem. We leverage recent advances of deep
convolutional neural networks to generate an ordered high-level sequence from a
whole word image, avoiding the difficult character segmentation problem. Then a
deep recurrent model, building on long short-term memory (LSTM), is developed
to robustly recognize the generated CNN sequences, departing from most existing
approaches recognising each character independently. Our model has a number of
appealing properties in comparison to existing scene text recognition methods:
(i) It can recognise highly ambiguous words by leveraging meaningful context
information, allowing it to work reliably without either pre- or
post-processing; (ii) the deep CNN feature is robust to various image
distortions; (iii) it retains the explicit order information in word image,
which is essential to discriminate word strings; (iv) the model does not depend
on pre-defined dictionary, and it can process unknown words and arbitrary
strings. Codes for the DTRN will be available.Comment: To appear in the 13th AAAI Conference on Artificial Intelligence
(AAAI-16), 201
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
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Traditional convolutional layers extract features from patches of data by
applying a non-linearity on an affine function of the input. We propose a model
that enhances this feature extraction process for the case of sequential data,
by feeding patches of the data into a recurrent neural network and using the
outputs or hidden states of the recurrent units to compute the extracted
features. By doing so, we exploit the fact that a window containing a few
frames of the sequential data is a sequence itself and this additional
structure might encapsulate valuable information. In addition, we allow for
more steps of computation in the feature extraction process, which is
potentially beneficial as an affine function followed by a non-linearity can
result in too simple features. Using our convolutional recurrent layers we
obtain an improvement in performance in two audio classification tasks,
compared to traditional convolutional layers. Tensorflow code for the
convolutional recurrent layers is publicly available in
https://github.com/cruvadom/Convolutional-RNN
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide
variations in their frequency content and temporal structure. Convolutional
neural networks (CNN) are able to extract higher level features that are
invariant to local spectral and temporal variations. Recurrent neural networks
(RNNs) are powerful in learning the longer term temporal context in the audio
signals. CNNs and RNNs as classifiers have recently shown improved performances
over established methods in various sound recognition tasks. We combine these
two approaches in a Convolutional Recurrent Neural Network (CRNN) and apply it
on a polyphonic sound event detection task. We compare the performance of the
proposed CRNN method with CNN, RNN, and other established methods, and observe
a considerable improvement for four different datasets consisting of everyday
sound events.Comment: Accepted for IEEE Transactions on Audio, Speech and Language
Processing, Special Issue on Sound Scene and Event Analysi
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