5,732 research outputs found
Adaptive Multi-Class Audio Classification in Noisy In-Vehicle Environment
With ever-increasing number of car-mounted electric devices and their
complexity, audio classification is increasingly important for the automotive
industry as a fundamental tool for human-device interactions. Existing
approaches for audio classification, however, fall short as the unique and
dynamic audio characteristics of in-vehicle environments are not appropriately
taken into account. In this paper, we develop an audio classification system
that classifies an audio stream into music, speech, speech+music, and noise,
adaptably depending on driving environments including highway, local road,
crowded city, and stopped vehicle. More than 420 minutes of audio data
including various genres of music, speech, speech+music, and noise are
collected from diverse driving environments. The results demonstrate that the
proposed approach improves the average classification accuracy up to 166%, and
64% for speech, and speech+music, respectively, compared with a non-adaptive
approach in our experimental settings
Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches
Radar sensors can be used for analyzing the induced frequency shifts due to
micro-motions in both range and velocity dimensions identified as micro-Doppler
(-D) and micro-Range (-R), respectively.
Different moving targets will have unique -D and
-R signatures that can be used for target classification.
Such classification can be used in numerous fields, such as gait recognition,
safety and surveillance. In this paper, a 25 GHz FMCW Single-Input
Single-Output (SISO) radar is used in industrial safety for real-time
human-robot identification. Due to the real-time constraint, joint
Range-Doppler (R-D) maps are directly analyzed for our classification problem.
Furthermore, a comparison between the conventional classical learning
approaches with handcrafted extracted features, ensemble classifiers and deep
learning approaches is presented. For ensemble classifiers, restructured range
and velocity profiles are passed directly to ensemble trees, such as gradient
boosting and random forest without feature extraction. Finally, a Deep
Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed
into the constructed network. DCNN shows a superior performance of 99\%
accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201
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