Unsupervised classification to improve the quality of a bird song recording dataset

Open audio databases such as Xeno-Canto are widely used to build datasets to explore bird song repertoire or to train models for automatic bird sound classification by deep learning algorithms. However, such databases suffer from the fact that bird sounds are weakly labelled: a species name is attributed to each audio recording without timestamps that provide the temporal localization of the bird song of interest. Manual annotations can solve this issue, but they are time consuming, expert-dependent, and cannot run on large datasets. Another solution consists in using a labelling function that automatically segments audio recordings before assigning a label to each segmented audio sample. Although labelling functions were introduced to expedite strong label assignment, their classification performance remains mostly unknown. To address this issue and reduce label noise (wrong label assignment) in large bird song datasets, we introduce a data-centric novel labelling function composed of three successive steps: 1) time-frequency sound unit segmentation, 2) feature computation for each sound unit, and 3) classification of each sound unit as bird song or noise with either an unsupervised DBSCAN algorithm or the supervised BirdNET neural network. The labelling function was optimized, validated, and tested on the songs of 44 West-Palearctic common bird species. We first showed that the segmentation of bird songs alone aggregated from 10% to 83% of label noise depending on the species. We also demonstrated that our labelling function was able to significantly reduce the initial label noise present in the dataset by up to a factor of three. Finally, we discuss different opportunities to design suitable labelling functions to build high-quality animal vocalizations with minimum expert annotation effort.     

Animal Sound Identifier (ASI): software for automated identification of vocal animals

Automated audio recording offers a powerful tool for acoustic monitoring schemes of bird, bat, frog and other vocal organisms, but the lack of automated species identification methods has made it difficult to fully utilise such data. We developed Animal Sound Identifier (ASI), a MATLAB software that performs probabilistic classification of species occurrences from field recordings. Unlike most previous approaches, ASI locates training data directly from the field recordings and thus avoids the need of pre‐defined reference libraries. We apply ASI to a case study on Amazonian birds, in which we classify the vocalisations of 14 species in 194 504 one‐minute audio segments using in total two weeks of expert time to construct, parameterise, and validate the classification models. We compare the classification performance of ASI (with training templates extracted automatically from field data) to that of monitoR (with training templates extracted manually from the Xeno‐Canto database), the results showing ASI to have substantially higher recall and precision rates.     

Cross-domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Deep learning based retinopathy classification with optical coherence tomography (OCT) images has recently attracted great attention. However, existing deep learning methods fail to work well when training and testing datasets are different due to the general issue of domain shift between datasets caused by different collection devices, subjects, imaging parameters, etc. To address this practical and challenging issue, we propose a novel deep domain adaptation (DDA) method to train a model on a labeled dataset and adapt it to an unlabelled dataset (collected under different conditions). It consists of two modules for domain alignment, that is, adversarial learning and entropy minimization. We conduct extensive experiments on three public datasets to evaluate the performance of the proposed method. The results indicate that there are large domain shifts between datasets, resulting a poor performance for conventional deep learning methods. The proposed DDA method can significantly outperform existing methods for retinopathy classification with OCT images. It achieves retinopathy classification accuracies of 0.915, 0.959 and 0.990 under three cross-domain (cross-dataset) scenarios. Moreover, it obtains a comparable performance with human experts on a dataset where no labeled data in this dataset have been used to train the proposed DDA method. We have also visualized the learnt features by using the t-distributed stochastic neighbor embedding (t-SNE) technique. The results demonstrate that the proposed method can learn discriminative features for retinopathy classification.     

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.     

中国鸟类的生活史和生态学特征数据集

中国现有1,445种鸟类, 是世界上鸟类物种数最多的国家之一。物种特征反映了生物有机体的功能和适合度, 在生态学、进化生物学和保护生物学研究中具有重要作用。但是, 目前还没有关于我国鸟类生活史、生态学和地理分布等物种特征的完整数据库。通过系统查阅文献和各种数据资料, 本文共收集整理出了中国1,445种鸟类17个功能特征数据: 体重、体长、嘴峰长、翅长、尾长、跗蹠长、食性、窝卵数、卵大小、卵体积、巢址、巢的类型、集群状况、迁徙状况、是否特有种、地理分布范围和分布省份等。在这些特征中, 除迁徙状况、是否特有种、地理分布范围和分布省份外, 其余特征数据均存在不同程度的缺失, 数据的完整度为60.83%‒100%。本数据库是目前关于中国鸟类最新和最全的物种特征数据库, 期望能为我国鸟类生态学、进化生物学、生物地理学、保护生物学等研究提供支持。     

Multi-view features fusion for birdsong classification

As important members of the ecosystem, birds are good monitors of the ecological environment. Bird recognition, especially birdsong recognition, has attracted more and more attention in the field of artificial intelligence. At present, traditional machine learning and deep learning are widely used in birdsong recognition. Deep learning can not only classify and recognize the spectrums of birdsong, but also be used as a feature extractor. Machine learning is often used to classify and recognize the extracted birdsong handcrafted feature parameters. As the data samples of the classifier, the feature of birdsong directly determines the performance of the classifier. Multi-view features from different methods of feature extraction can obtain more perfect information of birdsong. Therefore, aiming at enriching the representational capacity of single feature and getting a better way to combine features, this paper proposes a birdsong classification model based multi-view features, which combines the deep features extracted by convolutional neural network (CNN) and handcrafted features. Firstly, four kinds of handcrafted features are extracted. Those are wavelet transform (WT) spectrum, Hilbert-Huang transform (HHT) spectrum, short-time Fourier transform (STFT) spectrum and Mel-frequency cepstral coefficients (MFCC). Then CNN is used to extract the deep features from WT, HHT and STFT spectrum, and the minimal-redundancy-maximal-relevance (mRMR) to select optimal features. Finally, three classification models (random forest, support vector machine and multi-layer perceptron) are built with the deep features and handcrafted features, and the probability of classification results of the two types of features are fused as the new features to recognize birdsong. Taking sixteen species of birds as research objects, the experimental results show that the three classifiers obtain the accuracy of 95.49%, 96.25% and 96.16% respectively for the features of the proposed method, which are better than the seven single features and three fused features involved in the experiment. This proposed method effectively combines the deep features and handcrafted features from the perspectives of signal. The fused features can more comprehensively express the information of the bird audio itself, and have higher classification accuracy and lower dimension, which can effectively improve the performance of bird audio classification.     

Classifying and ranking audio clips to support bird species richness surveys

Advances in programmable field acoustic sensors provide immense data for bird species study. Manually searching for bird species present in these acoustic data is time-consuming. Although automated techniques have been used for species recognition in many studies, currently these techniques are prone to error due to the complexity of natural acoustics.In this paper we propose a smart sampling approach to help identify the maximum number of bird species while listening to the minimum amount of acoustic data. This approach samples audio clips in a manner that can direct bird species surveys more efficiently. First, a classifier is built to remove audio clips that are unlikely to contain birds; second, the remaining audio clips are ranked by a proxy for the number of species. This technique enables a more efficient determination of species richness.The experimental results show that the use of a classifier enables to remove redundant acoustic data and make our approach resilient to various weather conditions. By ranking audio clips classified as “Birds”, our method outperforms the currently best published strategy for finding bird species after 30 one-minute audio clip samples. Particularly after 60 samples, our method achieves 10 percentage points more species. Despite our focus on bird species, the proposed sampling approach is applicable to the search of other vocal species.     

Multilocus analysis of a taxonomically densely sampled dataset reveal extensive non-monophyly in the avian family Locustellidae

The phylogeny of most of the species in the avian passerine family Locustellidae is inferred using a Bayesian species tree approach (Bayesian Estimation of Species Trees, BEST), as well as a traditional Bayesian gene tree method (MrBayes), based on a dataset comprising one mitochondrial and four nuclear loci. The trees inferred by the different methods agree fairly well in topology, although in a few cases there are marked differences. Some of these discrepancies might be due to convergence problems for BEST (despite up to 1×10(9) iterations). The phylogeny strongly disagrees with the current taxonomy at the generic level, and we propose a revised classification that recognizes four instead of seven genera. These results emphasize the well known but still often neglected problem of basing classifications on non-cladistic evaluations of morphological characters. An analysis of an extended mitochondrial dataset with multiple individuals from most species, including many subspecies, suggest that several taxa presently treated as subspecies or as monotypic species as well as a few taxa recognized as separate species are in need of further taxonomic work.     

Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform

Classification and subsequent diagnosis of cardiac arrhythmias is an important research topic in clinical practice. Confirmation of the type of arrhythmia at an early stage is critical for reducing the risk and occurrence of cardiovascular events. Nevertheless, diagnoses must be confirmed by a combination of specialist experience and electrocardiogram (ECG) examination, which can lead to delays in diagnosis. To overcome such obstacles, this study proposes an automatic ECG classification algorithm based on transfer learning and continuous wavelet transform (CWT). The transfer learning method is able to transfer the domain knowledge and features of images to a EGG, which is a one-dimensional signal when a convolutional neural network (CNN) is used for classification. Meanwhile, CWT is used to convert a one-dimensional ECG signal into a two-dimensional signal map consisting of time-frequency components. Considering that morphological features can be helpful in arrhythmia classification, eight features related to the R peak of an ECG signal are proposed. These auxiliary features are integrated with the features extracted by the CNN and then fed into the fully linked arrhythmia classification layer. The CNN developed in this study can also be used for bird activity detection. The classification experiments were performed after converting the two types of audio files containing songbird sounds and those without songbird sounds from the NIPS4Bplus bird song dataset into the Mel spectrum. Compared to the most recent methods in the same field, the classification results improved accuracy and recognition by 11.67% and 11.57%, respectively.     

An automated multispecies bioacoustics sound classification method based on a nonlinear pattern: Twine-pat

Categorizing the bioacoustic and ecoacoustic properties of animals is great interest to biologists and ecologists. Also, multidisciplinary studies in engineering have significantly contributed to the development of acoustic analysis. Observing the animals living in the ecological environment provides information in many areas such as global warming, climate changes, monitoring of endangered animals, agricultural activities. However, the classification of bioacoustics sounds by manually is very hard. Therefore, automated bioacoustics sound classification is crucial for ecological science. This work presents a new multispecies bioacoustics sound dataset and novel machine learning model to classify bird and anuran species with sounds automatically. In this model, a new nonlinear textural feature generation function is presented by using twine cipher substitution box(S-box), and this feature generation function is named twine-pat. By using twine-pat and tunable Q-factor wavelet transform, a multilevel feature generation network is presented. Iterative ReliefF(IRF) is employed to select the most effective/valuable features. Two shallow classifiers are used to calculate results. Our presented model reached 98.75% accuracy by using k-nearest neighbor(kNN) classifier. The results obviously demonstrated the success of the presented model.     

Towards automatic wild animal monitoring: Identification of animal species in camera-trap images using very deep convolutional neural networks

Non-intrusive monitoring of animals in the wild is possible using camera trapping networks. The cameras are triggered by sensors in order to disturb the animals as little as possible. This approach produces a high volume of data (in the order of thousands or millions of images) that demands laborious work to analyze both useless (incorrect detections, which are the most) and useful (images with presence of animals). In this work, we show that as soon as some obstacles are overcome, deep neural networks can cope with the problem of the automated species classification appropriately. As case of study, the most common 26 of 48 species from the Snapshot Serengeti (SSe) dataset were selected and the potential of the Very Deep Convolutional neural networks framework for the species identification task was analyzed. In the worst-case scenario (unbalanced training dataset containing empty images) the method reached 35.4% Top-1 and 60.4% Top-5 accuracy. For the best scenario (balanced dataset, images containing foreground animals only, and manually segmented) the accuracy reached a 88.9% Top-1 and 98.1% Top-5, respectively. To the best of our knowledge, this is the first published attempt on solving the automatic species recognition on the SSe dataset. In addition, a comparison with other approaches on a different dataset was carried out, showing that the architectures used in this work outperformed previous approaches. The limitations of the method, drawbacks, as well as new challenges in automatic camera-trap species classification are widely discussed.     

中国43种蛙类鸣声特征数据集

无尾两栖动物的鸣声通常具有物种特异性,了解其鸣声特征信息,是利用生物声学进行物种多样性调查及物种监测的前提。本文汇总、整理了2012–2020年间利用高保真录音设备在野外记录的43种(隶属于7科26属)无尾两栖动物的鸣声数据,以及相应的鸣声采集信息。对音频文件进行降噪处理后,提供了由61个鸣声的波形图及语图组成的鸣声特征数据集。本数据集展示了鸣声的多种时域和频域信息,如单音节或多音节、音节数、音节时长、音节间隔、鸣声时长、主频、基频、谐波等,为我国无尾两栖类的声学研究、物种多样性调查及鸣声监测提供了数据支持。     

青藏高原植物群落样方数据集

为便于了解青藏高原植被特殊物种组成、群落特征及分布格局, 该文利用2018-2021年在青藏高原不同区域内调查的338个样地、共758个样方的数据, 分析了高原植物群落的物种组成、区系特征和植被分类, 整合形成青藏高原植物群落样方数据集。结果表明: 青藏高原高寒和温性植物群落758个样方中, 共有植物65科279属837种; 其中, 物种数最多的5个科依次是菊科(134种)、禾本科(88种)、豆科(75种)、蔷薇科(43种)和莎草科(40种), 物种数最多的5个属依次是蒿属(Artemisia, 29种)、马先蒿属(Pedicularis, 27种)、风毛菊属(Saussurea, 25种)、黄耆属(Astragalus, 23种)和早熟禾属(Poa, 23种)。植物区系主要由温带(145属)和世界广布(36属)的成分所组成。物种的生长型以草本(83.51%)和灌木(10.87%)为主, 草本和木本的生活型分别以多年生草本(88.23%)和落叶灌木(83.67%)为主。338个样地可以划分为4个植被型组, 10个植被型, 20个植被亚型, 78个群系组和117个群系, 其中草原群系34个, 草甸群系33个, 荒漠群系33个, 灌丛群系14个和针叶林群系3个。该数据集覆盖青藏高原绝大部分高寒灌丛、高寒草原、高寒草甸、高寒荒漠、温性草原和温性荒漠植被区域, 可为研究高原植被特征和地带性分异规律, 气候变化和人类活动对高原植被的影响及其生态恢复提供坚实的数据基础, 同时为下一代中国植被图的更新提供参考。     

Literature based species occurrence data of birds of northeast India

The northeast region of India is one of the world's most significant biodiversity hotspots. One of the richest bird areas in India, it is an important route for migratory birds and home to many endemic bird species. This paper describes a literature-based dataset of species occurrences of birds of northeast India. The occurrence records documented in the dataset are distributed across eleven states of India, viz.: Arunachal Pradesh, Assam, Bihar, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura, Uttar Pradesh and West Bengal. The geospatial scope of the dataset represents 24 to 29 degree North latitude and 78 to 94 degree East longitude, and it comprises over 2400 occurrence records. These records have been collated from scholarly literature published between1915 and 2008, especially from the Journal of the Bombay Natural History Society (JBNHS). The temporal scale of the dataset represents bird observations recorded between 1909 and 2007. The dataset has been developed by employing MS Excel. The key elements in the database are scientific name, taxonomic classification, temporal and geospatial details including geo-coordinate precision, data collector, basis of record and primary source of the data record. The temporal and geospatial quality of more than 50% of the data records has been enhanced retrospectively. Where possible, data records are annotated with geospatial coordinate precision to the nearest minute. This dataset is being constantly updated with the addition of new data records, and quality enhancement of documented occurrences. The dataset can be used in species distribution and niche modeling studies. It is planned to expand the scope of the dataset to collate bird species occurrences across the Indian peninsula.     

北京维管植物编目和分布数据集

区域性维管植物编目对于该区域内植物多样性保护及植物资源可持续利用具有重要意义。北京作为中国首都, 尽管编目工作早在20世纪60年代就得以开展, 但近30年来没有进行系统更新。现有数据零散、不系统, 相关编目进展甚至已经落后于周边地区。本文在《北京植物志(1992年修订版)》的基础上, 结合多年实际野外调查, 通过系统检索文献资料对现有编目数据进行查漏补缺(补充新分类群、新记录)、修订名称(基于新分类修订成果)、更新分类系统(采用基于分子数据的新分类系统), 并添加物种等级、分布状态、生长状态、室内/室外、分布区、了解程度及保护状况等相关信息, 最终完成北京维管植物编目和分布数据集(分为本土植物和外来植物两个表单, 其中外来植物主要基于志书和文献记载)。截至2021年12月31日, 该数据集共有数据2,883条(本土1,680条, 外来1,203条), 其中包含北京本土野生维管植物134科611属1,597个类群(1,440种3天然杂交种46亚种97变种11变型), 与《北京植物志(1992年修订版)》相比增加3科26属173种4亚种28变种11变型, 其中列入《国家重点保护野生植物名录(2021)》的有16种(一级仅1种), 列入《北京市重点保护野生植物名录(2008)》的有90种3亚种4变种; 收录外来维管植物137科581属1,184个类群(含992种及其他种下等级), 其中栽培植物854种19杂交种15亚种29变种2变型87栽培品种38栽培群, 逸生植物132种1亚种, 归化植物77种2变种, 入侵植物27种。编目数据显示, 北京本土野生维管植物多样性整体上并不高, 主要以广泛分布的常见种为主, 特有种、狭域种以及珍稀濒危种数量不多; 同时, 北京存在大量的外来植物(许多种类在《北京植物志》各版中已经收录), 这些植物也是北京维管植物多样性的重要组成部分, 但现有数据尚不完整。     

Novel neural network application for bacterial colony classification

Background

Bacterial colony morphology is the first step of classifying the bacterial species before sending them to subsequent identification process with devices, such as VITEK 2 automated system and mass spectrometry microbial identification system. It is essential as a pre-screening process because it can greatly reduce the scope of possible bacterial species and will make the subsequent identification more specific and increase work efficiency in clinical bacteriology. But this work needs adequate clinical laboratory expertise of bacterial colony morphology, which is especially difficult for beginners to handle properly. This study presents automatic programs for bacterial colony classification task, by applying the deep convolutional neural networks (CNN), which has a widespread use of digital imaging data analysis in hospitals. The most common 18 bacterial colony classes from Peking University First Hospital were used to train this framework, and other images out of these training dataset were utilized to test the performance of this classifier.

Results

The feasibility of this framework was verified by the comparison between predicted result and standard bacterial category. The classification accuracy of all 18 bacteria can reach 73%, and the accuracy and specificity of each kind of bacteria can reach as high as 90%.

Conclusions

The supervised neural networks we use can have more promising classification characteristics for bacterial colony pre-screening process, and the unsupervised network should have more advantages in revealing novel characteristics from pictures, which can provide some practical indications to our clinical staffs.

     

Semantic region of interest and species classification in the deep neural network feature domain

In this paper, we focus on animal object detection and species classification in camera-trap images collected in highly cluttered natural scenes. Using a deep neural network (DNN) model training for animal- background image classification, we analyze the input camera-trap images to generate a multi-level visual representation of the input image. We detect semantic regions of interest for animals from this representation using k-mean clustering and graph cut in the DNN feature domain. These animal regions are then classified into animal species using multi-class deep neural network model. According the experimental results, our method achieves 99.75% accuracy for classifying animals and background and 90.89% accuracy for classifying 26 animal species on the Snapshot Serengeti dataset, outperforming existing image classification methods.     

Winter bird communities in forest plantations in western England and their response to vegetation,growth stage and grazing

This paper presents the results of a study of wintering bird communities across a wide range of coniferous, broadleaved and mixed forest stands in the Forest of Dean, western England. Bird communities of broadleaved and coniferous woodland differed with respect to their species composition. The mean number of individual birds recorded increased linearly with woodland age and was not influenced by woodland type, stand size or the presence of grazing. Woodland age and type and the presence or absence of grazing all significantly influenced bird species richness and the proportions of the bird community made up by granivores, insectivores and omnivores. Broadleaved stands held more species than coniferous stands. Ungrazed stands held significantly more species, particularly seed-eating species, than grazed stands and this effect was independent of woodland type. Ordination was used to relate variation in tree species composition and stand structure to bird community composition. A larger number of species was associated with broadleaved stands and stands with abundant undergrowth than was associated with coniferous stands or stands with little undergrowth. Woodland age had less effect on bird community composition than the extent of undergrowth and the conifer to broadleaf ratio. The results of this work have relevance to the enhancement of winter bird communities in commercial forests.     

Automatic classification of Furnariidae species from the Paranaense Littoral region using speech-related features and machine learning

Over the last years, researchers have addressed the automatic classification of calling bird species. This is important for achieving more exhaustive environmental monitoring and for managing natural resources. Vocalisations help to identify new species, their natural history and macro-systematic relations, while computer systems allow the bird recognition process to be sped up and improved. In this study, an approach that uses state-of-the-art features designed for speech and speaker state recognition is presented. A method for voice activity detection was employed previous to feature extraction. Our analysis includes several classification techniques (multilayer perceptrons, support vector machines and random forest) and compares their performance using different configurations to define the best classification method. The experimental results were validated in a cross-validation scheme, using 25 species of the family Furnariidae that inhabit the Paranaense Littoral region of Argentina (South America). The results show that a high classification rate, close to 90%, is obtained for this family in this Furnariidae group using the proposed features and classifiers.