Data mining applied to acoustic bird species recognition

In this paper, we focus on identification of birds through their sounds. Bird species identification is gaining importance in field of ecological conservation and ornithology. . From three unknown bird sounds we identify a particular bird (Forest Owlet) through spectrum analysis. Since Forest Owlet is a critically endangered species in central India, its identification is important in their fragmented population census and thereby their conservation. Here, four different frequency domain analysis technique, viz., Mean Square Error (MSE) approach, Correlation analysis based on frequency shift and symmetry property, Wiener Filter theory and Mel Frequency Cepstral Coefficients (MFCC) approach are used. This paper present the comparison of these methods when implemented in MATLAB. Recorded bird calls from xentocanto website have been used in the above analysis.

PLoS ONE, 2014

Birdsong provides a unique model for understanding the behavioral and neural bases underlying complex sequential behaviors. However, birdsong analyses require laborious effort to make the data quantitatively analyzable. The previous attempts had succeeded to provide some reduction of human efforts involved in birdsong segment classification. The present study was aimed to further reduce human efforts while increasing classification performance. In the current proposal, a linear-kernel support vector machine was employed to minimize the amount of human-generated label samples for reliable element classification in birdsong, and to enable the classifier to handle highly-dimensional acoustic features while avoiding the over-fitting problem. Bengalese finch's songs in which distinct elements (i.e., syllables) were aligned in a complex sequential pattern were used as a representative test case in the neuroscientific research field. Three evaluations were performed to test (1) algorithm validity and accuracy with exploring appropriate classifier settings, (2) capability to provide accuracy with reducing amount of instruction dataset, and (3) capability in classifying large dataset with minimized manual labeling. The results from the evaluation (1) showed that the algorithm is 99.5% reliable in song syllables classification. This accuracy was indeed maintained in evaluation , even when the instruction data classified by human were reduced to one-minute excerpt (corresponding to 300-400 syllables) for classifying two-minute excerpt. The reliability remained comparable, 98.7% accuracy, when a large target dataset of whole day recordings (,30,000 syllables) was used. Use of a linear-kernel support vector machine showed sufficient accuracies with minimized manually generated instruction data in bird song element classification. The methodology proposed would help reducing laborious processes in birdsong analysis without sacrificing reliability, and therefore can help accelerating behavior and studies using songbirds.

The bird sound classification is a significant aspect of bioacoustics research, wildlife conservation, and an advanced system for analyzing bird populations. In this article, a framework is suggested to categorize bird species based on their sound vocals through acoustic signals. The framework comprises of fusion of features extracted from Acoustic Local Ternary Patterns (ALTPs), Mel-Frequency Cepstral Coefficients (MFCCs), and Linear Predictive Coding (LPC). After the signal representation is performed, we categorize the signal by different classifiers. We used a publicly available research-oriented Bird Sound dataset comprising 781 sound specimens from 18 diverse bird species. This research not only plays an important role in the field of bioacoustics but can also act as a valuable tool supporting wildlife conservation. Moreover, the vocalization detection method can also give an intuition of bird's behavior into their ecological roles, habitat inclination, and adaptation to environmental changes. The proposed sound classification technique achieved a higher accuracy rate of 97.4% using the Bagged Trees Ensemble method, in comparison with the research conducted on the same 18 classes of Bird Sound dataset. The experimental results depicted the reliability of a novel approach for the recognition of bird species based on their sounds.

Applied Acoustics, 2014

The primary purpose for pursuing this research is to present a modular approach that enables reliable automatic bird species identification on the basis of their sound emissions in the field. A practical and complete computer-based framework is proposed to detect and time-stamp particular bird species in continuous real field recordings. Acoustic detection of avian sounds can be used for the automatized monitoring of multiple bird taxa and querying in long-term recordings for species of interest for researchers, conservation practitioners, and decision makers, such as environmental indicator taxa and threatened species. This work describes two novel procedures and offers an open modular framework that detects and time-stamps online calls and songs of target bird species and is fast enough to report results in reasonable time for non-processed field recordings of many thousands files and is generic enough to accommodate any species. The framework is evaluated on two large corpora of real field data, targeting the calls and songs of American Robin Turdus migratorius, a Northamerican oscine passerine (true songbird) and the Common Kingfisher Alcedo atthis, a non-passerine species with a wide distribution throughout Eurasia and North Africa. With the aim of promoting the widespread use of digital autonomous recording units (ARUs) and species recognition technologies the processing code and a large corpus of audio recordings is provided in order to enable other researchers to perform and assess comparative experiments.

2000

In this paper a new method for automatic classification and recognition of bird sounds is presented. Our main idea is to study, how inharmonic and transient bird sounds can be recognized efficiently. The data consisted of sounds of eight bird species. Five species, the Mallard (Anas platyrhynchos), the Graylag Goose (Anser anser), the Corncrake (Crex crex), the River Warbler (Locustella fluviatilis), and the Magpie (Pica pica) have inharmonic sounds, whereas the remaining three reference species, the Quail (Coturnix coturnix), the Spotted Crake (Porzana porzana), and the Pygmy Owl (Glaucidium passerinum) have harmonic sounds. The wavelet analysis was selected due to its ability to preserve both frequency and temporal information, and its ability to analyse signals which contain discontinuities and sharp spikes. The feature vectors calculated with the proposed algorithm from the wavelet coefficients were used as the inputs of two neural networks, the self-organizing map (SOM) and the multilayer perceptron (MLP). The results were encouraging, for the unsupervised SOM network recognized 78% and the supervised MLP network 96% of the test sounds correctly.

2018

Small of the smallest living creature is the part of the whole ecosystem. Monitoring and maintaining this environmental health has become a task of prime importance, since human activity and climate change has placed the greater pressure on biodiversity. This helps in monitoring dangerous situations to planes caused by birds near airports and also in saving endangered species. Powerful audio signal processing techniques makes it possible to introduce automated methods for the detection of such vocalizations. The complete identification procedure essentially involves collection of recorded vocalizations of different species, audio pre-processing to remove the noise and silence, segmentation to select the most representative characterizing elements of the signal called syllable, extraction of different features commonly found in sound classification and speech recognition to obtain relevant characteristics, and decision procedures for identification, where Machine Learning (ML) algori...

— Automatic identification of bird species based on the chirping sounds of birds was experimented using feature extraction method and classification based on support vector machines (SVMs). The proposed technique followed the extraction of cepstral features on mel scale of each audio recording from the collected standard database. Extracted mel frequency cepstral coefficients (MFCCs) formed a feature matrix. This feature matrix was then trained and tested for efficient recognition of audio events from audio test signals. The classifier achieved upto 89.4% accuracy on a data set containing four species.

The 5th International Conference on Interdisciplinarity in Education ICIE'10, June 17-19, 2010, Tallinn, Estonia, 2010

The system which can monitor bird species based on automatic bird voices recognition is very useful in order to protect biodiversity of avifauna. In the paper database system together with an expert system and a recognition unit are described. Recognition unit (recognizer) recognizes bird species and bird communication signals from the digital signal registered in digital audio recorder. Information about bird species and communication system obtained from recognizer supported by an expert system is stored in the database system. Some initial results of recognition experiments are also given.

Tarnowskie Colloquia Naukowe, 2017

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 the 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 database 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.

pertanika journal of science and technology, 2021

Bird classification using audio data can be beneficial in assisting ornithologists, bird watchers and environmentalists. However, due to the complex environment in the jungles, it is difficult to identify birds by visual inspection. Hence, identification via acoustical means may be a better option in such an environment. This study aims to classify endemic Bornean birds using their sounds. Thirty-five (35) acoustic features have been extracted from the pre-recorded soundtracks of birds. In this paper, a novel approach for selecting an optimum number of features using Linear Discriminant Analysis (LDA) has been proposed to give better classification accuracy. It is found that using a Nearest Centroid (NC) technique with LDA produces the optimum classification results of bird sounds at 96.7% accuracy with reduced computational power. The low computational complexity is an added advantage for handheld portable devices with minimal computing power, which can be used in birdwatching expeditions. Comparison results have been provided with and without LDA using NC and Artificial Neural Network (ANN) classifiers. It has been demonstrated that both classifiers with LDA outperform those without LDA. Maximum accuracies for both NC and ANN with LDA, with NC and the ANN classifiers requiring 7 and 10 LDAs to achieve the optimum accuracy, respectively, are 96.7%. However, ANN classifier with LDA is more computationally complex. Hence, this is significant as the simpler NC classifier with LDA, which does not require expensive processing power, may be used on the portable and affordable device for bird classification purposes.