2,844 research outputs found
Robust sound event detection in bioacoustic sensor networks
Bioacoustic sensors, sometimes known as autonomous recording units (ARUs),
can record sounds of wildlife over long periods of time in scalable and
minimally invasive ways. Deriving per-species abundance estimates from these
sensors requires detection, classification, and quantification of animal
vocalizations as individual acoustic events. Yet, variability in ambient noise,
both over time and across sensors, hinders the reliability of current automated
systems for sound event detection (SED), such as convolutional neural networks
(CNN) in the time-frequency domain. In this article, we develop, benchmark, and
combine several machine listening techniques to improve the generalizability of
SED models across heterogeneous acoustic environments. As a case study, we
consider the problem of detecting avian flight calls from a ten-hour recording
of nocturnal bird migration, recorded by a network of six ARUs in the presence
of heterogeneous background noise. Starting from a CNN yielding
state-of-the-art accuracy on this task, we introduce two noise adaptation
techniques, respectively integrating short-term (60 milliseconds) and long-term
(30 minutes) context. First, we apply per-channel energy normalization (PCEN)
in the time-frequency domain, which applies short-term automatic gain control
to every subband in the mel-frequency spectrogram. Secondly, we replace the
last dense layer in the network by a context-adaptive neural network (CA-NN)
layer. Combining them yields state-of-the-art results that are unmatched by
artificial data augmentation alone. We release a pre-trained version of our
best performing system under the name of BirdVoxDetect, a ready-to-use detector
of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019;
revised August 2019; published October 201
The application of online transcranial random noise stimulation and perceptual learning in the improvement of visual functions in mild myopia
It has recently been demonstrated how perceptual learning, that is an improvement in a sensory/perceptual task upon practice, can be boosted by concurrent high-frequency transcranial random noise stimulation (tRNS). It has also been shown that perceptual learning can generalize and produce an improvement of visual functions in participants with mild refractive defects.
By using three different groups of participants (single-blind study), we tested the efficacy of a short training (8 sessions) using a single Gabor contrast-detection task with concurrent hf-tRNS in comparison with the same training with sham stimulation or hf-tRNS with no concurrent training, in improving visual acuity (VA) and contrast sensitivity (CS) of individuals with uncorrected mild myopia.
A short training with a contrast detection task is able to improve VA and CS only if coupled with hf-tRNS, whereas no effect on VA and marginal effects on CS are seen with the sole administration of hf-tRNS.
Our results support the idea that, by boosting the rate of perceptual learning via the modulation of neuronal plasticity, hf-tRNS can be successfully used to reduce the duration of the perceptual training and/or to increase its efficacy in producing perceptual learning and generalization to improved VA and CS in individuals with uncorrected mild myopia
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