50,637 research outputs found
Sound Recognition System Using Spiking and MLP Neural Networks
In this paper, we explore the capabilities of a sound classification
system that combines a Neuromorphic Auditory System for feature extraction
and an artificial neural network for classification. Two models of neural network
have been used: Multilayer Perceptron Neural Network and Spiking Neural
Network. To compare their accuracies, both networks have been developed and
trained to recognize pure tones in presence of white noise. The spiking neural
network has been implemented in a FPGA device. The neuromorphic auditory
system that is used in this work produces a form of representation that is analogous
to the spike outputs of the biological cochlea. Both systems are able to distinguish
the different sounds even in the presence of white noise. The recognition system
based in a spiking neural networks has better accuracy, above 91 %, even when
the sound has white noise with the same power.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
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
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
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
Many real-world time-series analysis problems are characterised by scarce
data. Solutions typically rely on hand-crafted features extracted from the time
or frequency domain allied with classification or regression engines which
condition on this (often low-dimensional) feature vector. The huge advances
enjoyed by many application domains in recent years have been fuelled by the
use of deep learning architectures trained on large data sets. This paper
presents an application of deep learning for acoustic event detection in a
challenging, data-scarce, real-world problem. Our candidate challenge is to
accurately detect the presence of a mosquito from its acoustic signature. We
develop convolutional neural networks (CNNs) operating on wavelet
transformations of audio recordings. Furthermore, we interrogate the network's
predictive power by visualising statistics of network-excitatory samples. These
visualisations offer a deep insight into the relative informativeness of
components in the detection problem. We include comparisons with conventional
classifiers, conditioned on both hand-tuned and generic features, to stress the
strength of automatic deep feature learning. Detection is achieved with
performance metrics significantly surpassing those of existing algorithmic
methods, as well as marginally exceeding those attained by individual human
experts.Comment: For data and software related to this paper, see
http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201
- …