3,747 research outputs found
Animal Sound Classification using Sequential Classifiers
Several authors have shown that the sounds of anurans can be used as an indicator of climate change. But
the recording, storage and further processing of a huge number of anuran’s sounds, distributed in time and
space, are required to obtain this indicator. It is therefore highly desirable to have algorithms and tools for
the automatic classification of the different classes of sounds. In this paper five different classification
methods are proposed, all of them based on the data mining domain, which try to take advantage of the
sound sequential behaviour. Its definition and comparison is undertaken using several approaches. The
sequential classifiers have revealed that they can obtain a better performance than their non-sequential
counterpart. The sliding window with an underlying decision tree has reached the best results in our tests,
even overwhelming the Hidden Markov Models usually employed in similar applications. A quite
remarkable overall classification performance has been obtained, a result even more relevant considering
the low quality of the analysed sounds.Junta de Andalucía TIC-570
Optimal Representation of Anuran Call Spectrum in Environmental Monitoring Systems Using Wireless Sensor Networks
The analysis and classification of the sounds produced by certain animal species, notably anurans, have revealed these amphibians to be a potentially strong indicator of temperature fluctuations and therefore of the existence of climate change. Environmental monitoring systems using Wireless Sensor Networks are therefore of interest to obtain indicators of global warming. For the automatic classification of the sounds recorded on such systems, the proper representation of the sound spectrum is essential since it contains the information required for cataloguing anuran calls. The present paper focuses on this process of feature extraction by exploring three alternatives: the standardized MPEG-7, the Filter Bank Energy (FBE), and the Mel Frequency Cepstral Coefficients (MFCC). Moreover, various values for every option in the extraction of spectrum features have been considered. Throughout the paper, it is shown that representing the frame spectrum with pure FBE offers slightly worse results than using the MPEG-7 features. This performance can easily be increased, however, by rescaling the FBE in a double dimension: vertically, by taking the logarithm of the energies; and, horizontally, by applying mel scaling in the filter banks. On the other hand, representing the spectrum in the cepstral domain, as in MFCC, has shown additional marginal improvements in classification performance.University of Seville: Telefónica Chair "Intelligence Networks
Temporally-aware algorithms for the classification of anuran sounds
Several authors have shown that the sounds of anurans can be used as an indicator of
climate change. Hence, the recording, storage and further processing of a huge
number of anuran sounds, distributed over time and space, are required in order to
obtain this indicator. Furthermore, it is desirable to have algorithms and tools for
the automatic classification of the different classes of sounds. In this paper, six
classification methods are proposed, all based on the data-mining domain, which
strive to take advantage of the temporal character of the sounds. The definition and
comparison of these classification methods is undertaken using several approaches.
The main conclusions of this paper are that: (i) the sliding window method attained
the best results in the experiments presented, and even outperformed the hidden
Markov models usually employed in similar applications; (ii) noteworthy overall
classification performance has been obtained, which is an especially striking result
considering that the sounds analysed were affected by a highly noisy background;
(iii) the instance selection for the determination of the sounds in the training dataset
offers better results than cross-validation techniques; and (iv) the temporally-aware
classifiers have revealed that they can obtain better performance than their nontemporally-aware
counterparts.Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain): excellence eSAPIENS number TIC 570
Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis
Fingerprinting Smart Devices Through Embedded Acoustic Components
The widespread use of smart devices gives rise to both security and privacy
concerns. Fingerprinting smart devices can assist in authenticating physical
devices, but it can also jeopardize privacy by allowing remote identification
without user awareness. We propose a novel fingerprinting approach that uses
the microphones and speakers of smart phones to uniquely identify an individual
device. During fabrication, subtle imperfections arise in device microphones
and speakers which induce anomalies in produced and received sounds. We exploit
this observation to fingerprint smart devices through playback and recording of
audio samples. We use audio-metric tools to analyze and explore different
acoustic features and analyze their ability to successfully fingerprint smart
devices. Our experiments show that it is even possible to fingerprint devices
that have the same vendor and model; we were able to accurately distinguish
over 93% of all recorded audio clips from 15 different units of the same model.
Our study identifies the prominent acoustic features capable of fingerprinting
devices with high success rate and examines the effect of background noise and
other variables on fingerprinting accuracy
A toolbox for animal call recognition
Monitoring the natural environment is increasingly important as habit degradation and climate change reduce theworld’s biodiversity.We have developed software tools and applications to assist ecologists with the collection and analysis of acoustic data at large spatial and temporal scales.One of our key objectives is automated animal call recognition, and our approach has three novel attributes. First, we work with raw environmental audio, contaminated by noise and artefacts and containing calls that vary greatly in volume depending on the animal’s proximity to the microphone. Second, initial experimentation suggested that no single recognizer could dealwith the enormous variety of calls. Therefore, we developed a toolbox of generic recognizers to extract invariant features for each call type. Third, many species are cryptic and offer little data with which to train a recognizer. Many popular machine learning methods require large volumes of training and validation data and considerable time and expertise to prepare. Consequently we adopt bootstrap techniques that can be initiated with little data and refined subsequently. In this paper, we describe our recognition tools and present results for real ecological problems
Evaluation of the Processing Times in Anuran Sound Classification
Nowadays, sound classification applications are becoming more common in the Wireless Acoustic Sensor Networks (WASN) scope. However, these architectures require special considerations, like looking for abalance between transmitted data and local processing.This article proposes an audio processing and classification scheme, focusing on WASN architectures.This article also analyzes in detail the time efficiency of the different stages involved (from acquisition to classification). This study provides useful information which makes it possible to choose the best tradeoff between processing time and classification result accuracy. This approach has been evaluated on a wide set of anurans songs registered in their own habitat. Among the conclusions of this work, there is an emphasis on the disparity in the classification and feature extraction and construction times for the different studied techniques,all of them notably depending on the over all feature number used.Consejería de Innovación, Ciencia y Empresa, Junta de Andalucía, Spain, through the Excellence Project eSAPIENS (Ref. TIC-5705
Improving classification algorithms by considering score series in wireless acoustic sensor networks
The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames,while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application
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