Robust acoustic bird recognition for habitat monitoring with wireless sensor networks

Boulmaiz, Amira; Messadeg, Djemil; Doghmane, Noureddine; Taleb‐Ahmed, Abdelmalik

doi:10.1007/s10772-016-9354-4

Cited by 22 publications

(20 citation statements)

References 47 publications

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“…The acoustic data obtained at fixed locations over time provides knowledge about ecosystem cycles and the data obtained from different locations can be compared. Their main limitations are the environmental noise and the energy consumption on sensors, requiring monitoring methods with low energy consumption [25].…”

Section: Wireless Acoustic Sensor Network For Wildlife Monitoringmentioning

confidence: 99%

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

Trapero-Bertran¹,

Alsina-Pagès²,

Tena

2019

Applied Sciences

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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.

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Section: Wireless Acoustic Sensor Network For Wildlife Monitoringmentioning

confidence: 99%

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

Trapero-Bertran¹,

Alsina-Pagès²,

Tena

2019

Applied Sciences

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“…Supplementary devices could be digital camera [5,21], weather station [5,33] and radar detector [21,28,31,33] being additional source information supporting acoustical bird monitoring process. Hardware equipment is more and more often connected in sensor networks [28]. An interesting device is a balloon where the electronic equipment can be placed [29].…”

Section: Acoustical Bird Monitoring -State Of the Artmentioning

confidence: 99%

“…Similar method to HMM is GMM [9,22] -also often used by researchers. Another methods include ANN [28], SVM [30] and DTW [15].…”

Section: Acoustical Bird Monitoring -State Of the Artmentioning

confidence: 99%

Towards the Automatic Acoustical Avian Monitoring System

et al. 2017

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One of the crucial aspects of the environmental protection is continuous monitoring of environment. Specific aspect is estimation of the bird species population. It is particularly important for bird species being in danger of extinction. Avian monitoring programs are time and money consuming actions which usually base on terrain expeditions. Certain remedy for this can be automatic acoustical avian monitoring system, described in the paper. Main components of the designed system are: digital audio recorder for bird voices acquisition, computer program automatically recognizing bird species by its signals emitted (voices or others) and object-relational database accessed via the Internet. Optional system components can be: digital camera and camcorder, bird attracting device, wireless data transmission module, power supply with solar panel, portable weather station. The system records bird voices and sends the recordings to the database. Recorded bird voices can be also provoked by the attracting device. Application of wireless data transmission module and power supply with solar panel allows long term operation of digital sound recorder in a hard accessible terrain. Recorded bird voices are analysed by the computer program and labelled with the automatically recognized bird species. Recognition accuracy of the program can be optionally enhanced by an expert system. Besides of labelled sound recordings, database can store also many other information like: photos and films accompanying recorded bird voices/ sounds, information about localization of observation/ recordings (GPS position, description of a place of an observation), information about bird features and behaviour, meteorological information, etc. Database on the base of geographical/ geological digital maps can generate actual maps of bird population (presence, number of individuals of each species). Moreover data-base can trigger alerts in case of rapidly decreasing bird population. It is also possible to obtain new knowledge about bird species with data mining methods. The paper presents collected data on observed bird species (audio recordings, photos and films) as well as results of experiments testing particular components of the automatic acoustical avian monitoring system.

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“…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. wireless acoustic sensor networks (WASN) becoming more and more popular, WASNs are commonly used for monitoring bird audio long-term [2]. Automatic bird species identification provides a suitable way to analyze the huge audio data from long-term monitoring programs [3].…”

mentioning

confidence: 99%

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

Xie

Xing

et al. 2019

Applied Sciences

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Featured Application: With this bird audio enhancement method, the bird audio collected through the WASN (Wireless Acoustic Sensor Network) can be processed to produce better quality audio, which is more suitable for bird species identification based on the bird audio, then a higher accuracy of identification will be achieved. 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 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. wireless acoustic sensor networks (WASN) becoming more and more popular, WASNs are commonly used for monitoring bird audio long-term [2]. Automatic bird species identification provides a suitable way to analyze the huge audio data from long-term monitoring programs [3]. However, the bird audio collected in nature is always accompanied by ambient noises, which consequently affect the accuracy of the bird species identification [4]. Therefore, audio enhancement should be carried out before identification, to improve identification accuracy.By exploiting the spatial properties of speech and noise signals, WASN techniques can significantly outperform single-channel techniques in terms of improving interference suppression and reducing speech distortion [5][6][7][8][9]. Although WASN has many advantages, it also has several challenges, such as the limited energy and calculation ability of each node. There are two kinds of audio enhancement algorithms for WASNs. The first is centralized, all the data is transferred to a so-called fusion center (FC) for further enhancement. The second is distributed, the enhancements are performed on all ...

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Robust acoustic bird recognition for habitat monitoring with wireless sensor networks

Cited by 22 publications

References 47 publications

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

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

Towards the Automatic Acoustical Avian Monitoring System

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

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