Multileveled ternary pattern and iterative ReliefF based bird sound classification

Abstract Birds may need to be identified for purposes such as environmental monitoring, follow-up, and species detection in the ecological area. Automatic sound classifiers have been used to perform species detection. Many methods have been presented in the literature to classify bird sounds with high accuracy. Nowadays, deep learning models have been used to classify data with high classification accuracy. However, these networks have high computational complexity. To obtain a highly accurate and lightweight classification model, a new multileveled and handcrafted features based machine learning model is presented. The presented automated bird sound classification model uses the multileveled ternary pattern (TP) feature generation, feature selection, and classification phases. The multileveled feature generation network can reach high classification accuracies since they generate high-level, low-level, and mid-level features. To construct levels, discrete wavelet transform (DWT) is employed to use the effectiveness of the DWT in bird sound classification. An improved version of the ReliefF, which is iterative ReliefF (IRF), is considered as feature selector. IRF selects the most informative features automatically, and these features are operated on linear discriminant (LD), k nearest neighbor (kNN), bagged tree (BT), and support vector machine (SVM) classifiers to calculate results of variable classifiers. The proposed multilevel TP and IRF based bird sound classification method reached 96.67% accuracy by using SVM on the 18 classes bird sound dataset.

[1]  D. Mills,et al.  Exploring the utility of traditional breed group classification as an explanation of problem-solving behavior of the domestic dog (Canis familiaris) , 2019, Journal of Veterinary Behavior.

[2]  Leandro Daniel Vignolo,et al.  Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning , 2017, Ecol. Informatics.

[3]  Sengul Dogan,et al.  A novel spiral pattern and 2D M4 pooling based environmental sound classification method , 2020 .

[4]  Luiz Eduardo Soares de Oliveira,et al.  Bird species identification using spectrogram and dissimilarity approach , 2018, Ecol. Informatics.

[5]  Tran Huy Dat,et al.  Multi-label bird classification using an ensemble classifier with simple features , 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

[6]  Rajkumar Palaniappan,et al.  Machine learning in lung sound analysis: a systematic review , 2013 .

[7]  Masato Suzuki,et al.  A new survey method using convolutional neural networks for automatic classification of bird calls , 2020, Ecol. Informatics.

[8]  Sandeep Singh Solanki,et al.  Automatic bird species recognition system using neural network based on spike , 2020 .

[9]  Roberto A. Santiago,et al.  Storage of auditory temporal patterns in the songbird telencephalon , 2007, Neurocomputing.

[10]  Lei Zhang,et al.  A subregion division based multi-objective evolutionary algorithm for SVM training set selection , 2020, Neurocomputing.

[11]  Juan Lavista Ferres,et al.  Multispecies bioacoustic classification using transfer learning of deep convolutional neural networks with pseudo-labeling , 2020 .

[12]  Sengul Dogan,et al.  Pyramid and multi kernel based local binary pattern for texture recognition , 2019, Journal of Ambient Intelligence and Humanized Computing.

[13]  Chin-Chuan Han,et al.  Automatic Classification of Bird Species From Their Sounds Using Two-Dimensional Cepstral Coefficients , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[14]  Todor Ganchev,et al.  Automated acoustic detection of Vanellus chilensis lampronotus , 2015, Expert Syst. Appl..

[15]  Jie Xie,et al.  Handcrafted features and late fusion with deep learning for bird sound classification , 2019, Ecol. Informatics.

[16]  Justin Salamon,et al.  Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification , 2016, IEEE Signal Processing Letters.

[17]  M. Pescador,et al.  Effects of traffic noise on paserine populations in Mediterranean wooded pastures , 2004 .

[18]  Klaus Riede,et al.  Automatic bird sound detection in long real-field recordings: Applications and tools , 2014 .

[19]  Héctor Corrada Bravo,et al.  Automated classification of bird and amphibian calls using machine learning: A comparison of methods , 2009, Ecol. Informatics.

[20]  Germán Castellanos-Domínguez,et al.  Enhancing the dissimilarity-based classification of birdsong recordings , 2016, Ecol. Informatics.

[21]  Sengul Dogan,et al.  A novel ensemble local graph structure based feature extraction network for EEG signal analysis , 2020, Biomed. Signal Process. Control..

[22]  Hervé Glotin,et al.  LifeCLEF 2017 Lab Overview: Multimedia Species Identification Challenges , 2017, CLEF.

[23]  Chia-Feng Juang,et al.  Birdsong recognition using prediction-based recurrent neural fuzzy networks , 2007, Neurocomputing.

[24]  Pingkun Yan,et al.  Hierarchical incorporation of shape and shape dynamics for flying bird detection , 2014, Neurocomputing.

[25]  Ludmila I. Kuncheva,et al.  Adaptive Learning Rate for Online Linear Discriminant Classifiers , 2008, SSPR/SPR.

[26]  Todor Ganchev,et al.  Audio parameterization with robust frame selection for improved bird identification , 2015, Expert Syst. Appl..

[27]  Brendan A. Wintle,et al.  Metrics of progress in the understanding and management of threats to Australian birds , 2018, Conservation biology : the journal of the Society for Conservation Biology.

[28]  Robert Hickling,et al.  Studies of sound transmission in various types of stored grain for acoustic detection of insects , 1997 .

[29]  Juan Lavista Ferres,et al.  A pipeline for identification of bird and frog species in tropical soundscape recordings using a convolutional neural network , 2020, Ecol. Informatics.

[30]  Pierre Aumond,et al.  Probabilistic modeling framework for multisource sound mapping , 2018, Applied Acoustics.

[31]  Ping Du,et al.  A segmentation algorithm for zebra finch song at the note level , 2006, Neurocomputing.

[32]  Ying Li,et al.  Adaptive energy detection for bird sound detection in complex environments , 2015, Neurocomputing.

[33]  M. Hecker,et al.  An Early–Life Stage Alternative Testing Strategy for Assessing the Impacts of Environmental Chemicals in Birds , 2019, Environmental toxicology and chemistry.

[34]  Paul Roe,et al.  Acoustic classification of Australian frogs based on enhanced features and machine learning algorithms , 2016 .

[35]  Zhixin Chen,et al.  Semi-automatic classification of bird vocalizations using spectral peak tracks. , 2006, The Journal of the Acoustical Society of America.

[36]  Paul Roe,et al.  Using multi-label classification for acoustic pattern detection and assisting bird species surveys , 2016 .

[37]  Sengul Dogan,et al.  An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image , 2020, Chemometrics and Intelligent Laboratory Systems.

[38]  Abeer Alwan,et al.  Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training data. , 2015, The Journal of the Acoustical Society of America.

[39]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[40]  Peter Jancovic,et al.  Acoustic Recognition of Multiple Bird Species Based on Penalized Maximum Likelihood , 2015, IEEE Signal Processing Letters.

[41]  Stavros Ntalampiras,et al.  Bird species identification via transfer learning from music genres , 2018, Ecol. Informatics.

[42]  Arnaud Can,et al.  The future of urban sound environments: Impacting mobility trends and insights for noise assessment and mitigation , 2020 .

[43]  Hossein Karshenas,et al.  KNN-based multi-label twin support vector machine with priority of labels , 2018, Neurocomputing.

[44]  Leif E. Peterson.  K-nearest neighbor , 2009, Scholarpedia.

[45]  Xin Zhang,et al.  Spectrogram-frame linear network and continuous frame sequence for bird sound classification , 2019, Ecol. Informatics.

[46]  S. Sathiya Keerthi,et al.  A fast iterative nearest point algorithm for support vector machine classifier design , 2000, IEEE Trans. Neural Networks Learn. Syst..

[47]  William J. Davies,et al.  Generalisation in Environmental Sound Classification: The ‘Making Sense of Sounds’ Data Set and Challenge , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[48]  Philip K. McKinley,et al.  Ensemble extraction for classification and detection of bird species , 2010, Ecol. Informatics.

[49]  Kun Qian,et al.  A Bag of Wavelet Features for Snore Sound Classification , 2019, Annals of Biomedical Engineering.

[50]  Zhiyong Xu,et al.  Automated bird acoustic event detection and robust species classification , 2017, Ecol. Informatics.

[51]  Chin-Chuan Han,et al.  Local Wavelet Acoustic Pattern: A Novel Time–Frequency Descriptor for Birdsong Recognition , 2018, IEEE Transactions on Multimedia.