810,360 research outputs found
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Aerial scene recognition is a fundamental task in remote sensing and has
recently received increased interest. While the visual information from
overhead images with powerful models and efficient algorithms yields
considerable performance on scene recognition, it still suffers from the
variation of ground objects, lighting conditions etc. Inspired by the
multi-channel perception theory in cognition science, in this paper, for
improving the performance on the aerial scene recognition, we explore a novel
audiovisual aerial scene recognition task using both images and sounds as
input. Based on an observation that some specific sound events are more likely
to be heard at a given geographic location, we propose to exploit the knowledge
from the sound events to improve the performance on the aerial scene
recognition. For this purpose, we have constructed a new dataset named AuDio
Visual Aerial sceNe reCognition datasEt (ADVANCE). With the help of this
dataset, we evaluate three proposed approaches for transferring the sound event
knowledge to the aerial scene recognition task in a multimodal learning
framework, and show the benefit of exploiting the audio information for the
aerial scene recognition. The source code is publicly available for
reproducibility purposes.Comment: ECCV 202
Robust Sound Event Classification using Deep Neural Networks
The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise.
This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques
Automatic Environmental Sound Recognition: Performance versus Computational Cost
In the context of the Internet of Things (IoT), sound sensing applications
are required to run on embedded platforms where notions of product pricing and
form factor impose hard constraints on the available computing power. Whereas
Automatic Environmental Sound Recognition (AESR) algorithms are most often
developed with limited consideration for computational cost, this article seeks
which AESR algorithm can make the most of a limited amount of computing power
by comparing the sound classification performance em as a function of its
computational cost. Results suggest that Deep Neural Networks yield the best
ratio of sound classification accuracy across a range of computational costs,
while Gaussian Mixture Models offer a reasonable accuracy at a consistently
small cost, and Support Vector Machines stand between both in terms of
compromise between accuracy and computational cost
Two and three-dimensional visual articulatory models for pronunciation training and for treatment of speech disorders
Visual articulatory models can be used for visualizing vocal tract articulatory speech movements. This information may be helpful in pronunciation training or in therapy of speech disorders. For testing this hypothesis, speech recognition rates were quantified for mute animations of vocalic and consonantal speech movements generated by a 2D and a 3D visual articulatory model. The visually based speech sound recognition test (mimicry test) was performed by two groups of eight children (five to eight years old) matched in age and sex. The children were asked to mimic the visually produced mute speech movement animations for different speech sounds. Recognition rates stay significantly above chance but indicate no significant difference for each of the two models. Children older than 5 years are capable of interpreting vocal tract articulatory speech sound movements without any preparatory training in a speech adequate way. The complex 3D-display of vocal tract articulatory movements provides no significant advantage in comparison to the visually simpler 2D-midsagittal displays of vocal tract articulatory movements
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