207,353 research outputs found
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition
Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural network with long short-term memory (LSTM) architecture to adaptively
estimate real-time beamforming filter coefficients to cope with non-stationary
environmental noise and dynamic nature of source and microphones positions
which results in a set of timevarying room impulse responses. The LSTM adaptive
beamformer is jointly trained with a deep LSTM acoustic model to predict senone
labels. Further, we use hidden units in the deep LSTM acoustic model to assist
in predicting the beamforming filter coefficients. The proposed system achieves
7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real
evaluation set.Comment: in 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
Integrating model checking with HiP-HOPS in model-based safety analysis
The ability to perform an effective and robust safety analysis on the design of modern safety–critical systems is crucial. Model-based safety analysis (MBSA) has been introduced in recent years to support the assessment of complex system design by focusing on the system model as the central artefact, and by automating the synthesis and analysis of failure-extended models. Model checking and failure logic synthesis and analysis (FLSA) are two prominent MBSA paradigms. Extensive research has placed emphasis on the development of these techniques, but discussion on their integration remains limited. In this paper, we propose a technique in which model checking and Hierarchically Performed Hazard Origin and Propagation Studies (HiP-HOPS) – an advanced FLSA technique – can be applied synergistically with benefit for the MBSA process. The application of the technique is illustrated through an example of a brake-by-wire system
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
We present a unified framework for understanding human social behaviors in
raw image sequences. Our model jointly detects multiple individuals, infers
their social actions, and estimates the collective actions with a single
feed-forward pass through a neural network. We propose a single architecture
that does not rely on external detection algorithms but rather is trained
end-to-end to generate dense proposal maps that are refined via a novel
inference scheme. The temporal consistency is handled via a person-level
matching Recurrent Neural Network. The complete model takes as input a sequence
of frames and outputs detections along with the estimates of individual actions
and collective activities. We demonstrate state-of-the-art performance of our
algorithm on multiple publicly available benchmarks
Quantitative shadowgraphy and proton radiography for large intensity modulations
Shadowgraphy is a technique widely used to diagnose objects or systems in
various fields in physics and engineering. In shadowgraphy, an optical beam is
deflected by the object and then the intensity modulation is captured on a
screen placed some distance away. However, retrieving quantitative information
from the shadowgrams themselves is a challenging task because of the non-linear
nature of the process. Here, a novel method to retrieve quantitative
information from shadowgrams, based on computational geometry, is presented for
the first time. This process can be applied to proton radiography for electric
and magnetic field diagnosis in high-energy-density plasmas and has been
benchmarked using a toroidal magnetic field as the object, among others. It is
shown that the method can accurately retrieve quantitative parameters with
error bars less than 10%, even when caustics are present. The method is also
shown to be robust enough to process real experimental results with simple pre-
and post-processing techniques. This adds a powerful new tool for research in
various fields in engineering and physics for both techniques
An integrated neuro-mechanical model of C. elegans forward locomotion
One of the most tractable organisms for the study of nervous
systems is the nematode Caenorhabditis elegans, whose locomotion in
particular has been the subject of a number of models. In this paper we
present a first integrated neuro-mechanical model of forward locomotion.
We find that a previous neural model is robust to the addition of a
body with mechanical properties, and that the integrated model produces
oscillations with a more realistic frequency and waveform than the neural
model alone. We conclude that the body and environment are likely to
be important components of the worm’s locomotion subsystem
Robust Spoken Language Understanding for House Service Robots
Service robotics has been growing significantly in thelast years, leading to several research results and to a numberof consumer products. One of the essential features of theserobotic platforms is represented by the ability of interactingwith users through natural language. Spoken commands canbe processed by a Spoken Language Understanding chain, inorder to obtain the desired behavior of the robot. The entrypoint of such a process is represented by an Automatic SpeechRecognition (ASR) module, that provides a list of transcriptionsfor a given spoken utterance. Although several well-performingASR engines are available off-the-shelf, they operate in a generalpurpose setting. Hence, they may be not well suited in therecognition of utterances given to robots in specific domains. Inthis work, we propose a practical yet robust strategy to re-ranklists of transcriptions. This approach improves the quality of ASRsystems in situated scenarios, i.e., the transcription of roboticcommands. The proposed method relies upon evidences derivedby a semantic grammar with semantic actions, designed tomodel typical commands expressed in scenarios that are specificto human service robotics. The outcomes obtained throughan experimental evaluation show that the approach is able toeffectively outperform the ASR baseline, obtained by selectingthe first transcription suggested by the AS
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