Applied Sciences

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11 pages, 1161 KiB

Open AccessArticle

Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

by Juan J. Noda, Carlos M. Travieso-González, David Sánchez-Rodríguez and Jesús B. Alonso-Hernández

Appl. Sci. 2019, 9(19), 4097; https://doi.org/10.3390/app9194097 - 01 Oct 2019

Cited by 25 | Viewed by 3788

This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of [...] Read more.

This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity. The study proposes a sound parameterization technique designed specifically for identification and classification of acoustic signals of insects using Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC). These two sets of coefficients are evaluated individually as has been done in previous studies and have been compared with the fusion proposed in this work, showing an outstanding increase in identification and classification at species level reaching a success rate of 98.07% on 343 insect species. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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27 pages, 1305 KiB

Open AccessArticle

Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognition on Embedded Systems

by Bruno da Silva, Axel W. Happi, An Braeken and Abdellah Touhafi

Appl. Sci. 2019, 9(18), 3885; https://doi.org/10.3390/app9183885 - 16 Sep 2019

Cited by 31 | Viewed by 3971

Abstract

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when [...] Read more.

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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21 pages, 15269 KiB

Open AccessArticle

Pipistrellus pipistrellus and Pipistrellus pygmaeus in the Iberian Peninsula: An Annotated Segmented Dataset and a Proof of Concept of a Classifier in a Real Environment

by Marta Bertran, Rosa Ma Alsina-Pagès and Elena Tena

Appl. Sci. 2019, 9(17), 3467; https://doi.org/10.3390/app9173467 - 22 Aug 2019

Cited by 2 | Viewed by 3508

Abstract

Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound [...] Read more.

Bats have an important role in the ecosystem, and therefore an effective detection of their prevalence can contribute to their conservation. At present, the most commonly methodology used in the study of bats is the analysis of echolocation calls. However, many other ultrasound signals can be simultaneously recorded, and this makes species location and identification a long and difficult task. This field of research could be greatly improved through the use of bioacoustics which provide a more accurate automated detection, identification and count of the wildlife of a particular area. We have analyzed the calls of two bat species—Pipistrellus pipistrellus and Pipistrellus pygmaeus—both of which are common types of bats frequently found in the Iberian Peninsula. These two cryptic species are difficult to identify by their morphological features, but are more easily identified by their echolocation calls. The real-life audio files have been obtained by an Echo Meter Touch Pro 1 bat detector. Time-expanded recordings of calls were first classified manually by means of their frequency, duration and interpulse interval. In this paper, we first detail the creation of a dataset with three classes, which are the two bat species but also the silent intervals. This dataset can be useful to work in mixed species environment. Afterwards, two automatic bat detection and identification machine learning approaches are described, in a laboratory environment, which represent the previous step to real-life in an urban scenario. The priority in that approaches design is the identification using short window analysis in order to detect each bat pulse. However, given that we are concerned with the risks of automatic identification, the main aim of the project is to accelerate the manual ID process for the specialists in the field. The dataset provided will help researchers develop automatic recognition systems for a more accurate identification of the bat species in a laboratory environment, and in a near future, in an urban environment, where those two bat species are common. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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16 pages, 2453 KiB

Open AccessArticle

Improved Distributed Minimum Variance Distortionless Response (MVDR) Beamforming Method Based on a Local Average Consensus Algorithm for Bird Audio Enhancement in Wireless Acoustic Sensor Networks

by Jiangjian Xie, Xingguang Li, Zhaoliang Xing, Bowen Zhang, Weidong Bao and Junguo Zhang

Appl. Sci. 2019, 9(15), 3153; https://doi.org/10.3390/app9153153 - 02 Aug 2019

Cited by 5 | Viewed by 2374

Abstract

Currently, wireless acoustic sensor networks (WASN) are commonly used for wild bird monitoring. To better realize the automatic identification of birds during monitoring, the enhancement of bird audio is essential in nature. Currently, distributed beamformer is the most suitable method for bird audio [...] Read more.

Currently, wireless acoustic sensor networks (WASN) are commonly used for wild bird monitoring. To better realize the automatic identification of birds during monitoring, the enhancement of bird audio is essential in nature. Currently, distributed beamformer is the most suitable method for bird audio enhancement of WASN. However, there are still several disadvantages of this method, such as large noise residue and slow convergence rate. To overcome these shortcomings, an improved distributed minimum variance distortionless response (IDMVDR) beamforming method for bird audio enhancement in WASN is proposed in this paper. In this method, the average metropolis weight local average consensus algorithm is first introduced to increase the consensus convergence rate, then a continuous spectrum update algorithm is proposed to estimate the noise power spectral density (PSD) to improve the noise reduction performance. Lastly, an MVDR beamformer is introduced to enhance the bird audio. Four different network topologies of the WASNs were considered, and the bird audio enhancement was performed on these WASNs to validate the effectiveness of the proposed method. Compared with two classical methods, the results show that the Segmental signal to noise ratio (SegSNR), mean square error (MSE), and perceptual evaluation of speech quality (PESQ) obtained by the proposed method are better and the consensus rate is faster, which means that the proposed method performs better in audio quality and convergence rate, and therefore it is suitable for WASN with dynamic topology. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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11 pages, 620 KiB

Open AccessFeature PaperArticle

Computation of Psycho-Acoustic Annoyance Using Deep Neural Networks

by Jesus Lopez-Ballester, Adolfo Pastor-Aparicio, Jaume Segura-Garcia, Santiago Felici-Castell and Maximo Cobos

Appl. Sci. 2019, 9(15), 3136; https://doi.org/10.3390/app9153136 - 02 Aug 2019

Cited by 6 | Viewed by 3217

Abstract

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be [...] Read more.

Psycho-acoustic parameters have been extensively used to evaluate the discomfort or pleasure produced by the sounds in our environment. In this context, wireless acoustic sensor networks (WASNs) can be an interesting solution for monitoring subjective annoyance in certain soundscapes, since they can be used to register the evolution of such parameters in time and space. Unfortunately, the calculation of the psycho-acoustic parameters involved in common annoyance models implies a significant computational cost, and makes difficult the acquisition and transmission of these parameters at the nodes. As a result, monitoring psycho-acoustic annoyance becomes an expensive and inefficient task. This paper proposes the use of a deep convolutional neural network (CNN) trained on a large urban sound dataset capable of efficiently predicting psycho-acoustic annoyance from raw audio signals continuously. We evaluate the proposed regression model and compare the resulting computation times with the ones obtained by the conventional direct calculation approach. The results confirm that the proposed model based on CNN achieves high precision in predicting psycho-acoustic annoyance, predicting annoyance values with an average quadratic error of around 3%. It also achieves a very significant reduction in processing time, which is up to 300 times faster than direct calculation, making CNN designed a clear exponent to work in IoT devices. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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25 pages, 6738 KiB

Open AccessFeature PaperArticle

Acoustic Beam Characterization and Selection for Optimized Underwater Communication

by Akram Ahmed and Mohamed Younis

Appl. Sci. 2019, 9(13), 2740; https://doi.org/10.3390/app9132740 - 06 Jul 2019

Cited by 3 | Viewed by 2854

Abstract

To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling [...] Read more.

To increase underwater acoustic signal detectability and conserve energy, nodes leverage directional transmissions. In addition, nodes operate in a three-dimensional (3D) environment that is categorized as inhomogeneous where a propagating signal changes its direction based on the observed sound speed profile (SSP). Coupling 3D directional transmission with frequent node drifts and the varying underwater SSP complicates the process of selecting suitable transmission angles to maintain underwater communication links. Fundamentally, utilizing directional transmission while nodes are drifting causes breaks in established communication links and thus nodes need to find new angles to reestablish these links. Moreover, selecting arbitrary transmission angles may lead to overlapping beams or result in leaving an underwater region uncovered. To tackle the abovementioned challenges, this paper proposes an autonomous beam selection approach that optimizes underwater communication by selecting non-overlapping beams while mitigating the possibility of missing a region, i.e., maximize coverage. Such optimization is achieved by utilizing a structured angle selection mechanism that accounts for the capability of the used transducer. Moreover, we introduce an algorithm suited for resource constrained nodes to classify rays into different types. Then we divide the underwater medium into regions where each region is identified by the limits of the coverage area of each ray type. Finally, we utilize the limits of these regions to aid nodes in selecting the best ray to reestablish communication with drifted nodes. We validate our contribution through simulation where actual SSPs are leveraged to validate the beam classification process. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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16 pages, 653 KiB

Open AccessArticle

A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

by Hui Wang, Youming Li, Liliang Zhang, Yexian Fan and Zhiliang Li

Appl. Sci. 2019, 9(7), 1479; https://doi.org/10.3390/app9071479 - 09 Apr 2019

Cited by 6 | Viewed by 2429

Abstract

The self-deployment of nodes with non-uniform coverage in underwater acoustic sensor networks (UASNs) is challenging because it is difficult to access the three-dimensional underwater environment. The problem is further complicated if network connectivity needs to be considered. In order to solve the optimization [...] Read more.

The self-deployment of nodes with non-uniform coverage in underwater acoustic sensor networks (UASNs) is challenging because it is difficult to access the three-dimensional underwater environment. The problem is further complicated if network connectivity needs to be considered. In order to solve the optimization problem of sensor network node deployment, we propose a maximum coverage and connectivity self-deployment algorithm that is based on ant colony optimization (MCC-ACO). We carry out the greedy strategy, improve the path selection probability and pheromone update system, and propose a self-deployment algorithm based on the foundation of standard ant colony optimization algorithms, so as to achieve energy-saving optimization coverage of target events. The main characteristic of the MCC-ACO algorithm is that it fully considers the effects of the changes in the event quantities and the random distribution of the nodes on the deployment effect of the nodes, ensures that every deployed node can be connected to the sink, and achieves the matching of node distribution density and event distribution. Therefore, the MCC-ACO algorithm has great practical value. A large number of comparative simulation experiments show that the algorithm can effectively realize the self-deployment problem of underwater sensor nodes. In addition, the paper also gives the impact of changes in the number of events in the network on the deployment effect. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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11 pages, 2236 KiB

Open AccessArticle

A Weighted Turbo Equalized Multi-Band Underwater Wireless Acoustic Communications

by Hui-Su Lee, Chang-Uk Baek, Ji-Won Jung and Wan-Jin Kim

Appl. Sci. 2018, 8(10), 1711; https://doi.org/10.3390/app8101711 - 20 Sep 2018

Cited by 7 | Viewed by 2531

Abstract

The multi-band UWAS (Underwater Wireless Acoustic Sensor) communication techniques are effective in terms of performance and throughput efficiency because they can overcome selective frequency fading by allocating the same data to different frequency bands, in an environment of rapidly changing channel transfer characteristic. [...] Read more.

The multi-band UWAS (Underwater Wireless Acoustic Sensor) communication techniques are effective in terms of performance and throughput efficiency because they can overcome selective frequency fading by allocating the same data to different frequency bands, in an environment of rapidly changing channel transfer characteristic. However, the multi-band configuration may have a performance worse than the single-band one because performance degradation in one particular band affects the output from all the other bands. This problem can be solved by using a receiving end that analyzes the error rates of each band, sets threshold values and allocates the lower weights to the inferior bands. There are many methods of setting threshold values. In this paper, we proposed an algorithm to set the threshold value by using the preamble error rates, which are known data that have to be transmitted and received. In addition, we have analyzed the efficiency of multi-band transmission scheme in the UWAS communication by applying 1~4 number of multi-bands, using turbo pi codes, with a coding rate of 1/3. We evaluated the performance of the proposed multi-bands transmission model in real underwater environments. Experimental results showed that the performance increased as the number of multiple bands increased. Furthermore, the performance of multi-band was improved when the proposed threshold algorithm was applied. Full article

(This article belongs to the Special Issue Recent Advances on Wireless Acoustic Sensor Networks (WASN))

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