Identification of bird species from audio records is one of the challenging tasks due to the existence of multiple species in the same recording, noise in the background, and long-term recording. Besides, choosing a proper acoustic feature from audio recording for bird species classification is another problem. In this paper, a hybrid method is represented comprising both traditional signal processing and a deep learning-based approach to classify bird species from audio recordings of diverse sources and types. Besides, a detailed study with 34 different features helps to select the proper feature set for classification and analysis in real-time applications. Moreover, the proposed deep neural network uses both acoustic and temporal feature learning. The proposed method starts with detecting voice activity from the raw signal, followed by extracting short-term features from the processed recording using 50 ms (with 25ms overlapping) time windows. Later, the short-term features are reshaped using second stage (non-overlapping) windowing to be trained through a distributed 2D Convolutional Neural Network (CNN) that forwards the output features to a Long and Short Term Memory (LSTM) Network. Then a final dense layer classifies the bird species. For the 10 class classifier, the highest accuracy achieved was 90.45\% for a feature set consisting of 13 Mel Frequency Cepstral Coefficients (MFCCs) and 12 Chroma Vectors. The corresponding specificity and AUC scores are 98.94\% and 94.09\%, respectively.
DB3V: A Dialect Dominated Dataset of Bird Vocalisation for Cross-corpus Bird Species Recognition
University of Augsburg（奥格斯堡大学）；Beijing Forestry University（北京林业大学）；Technical University of Munich（慕尼黑工业大学）；Hume AI（休谟人工智能公司）；Imperial College London（伦敦帝国理工学院）；Munich Data Science Institute（慕尼黑数据科学研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Based on the transfer learning, we design a bird species identification model that uses the VGG-16 model (pretrained on ImageNet) for feature extraction, then a classifier consisting of two fully-connected hidden layers and a Softmax layer is attached. We compare the performance of the proposed model with the original VGG16 model. The results show that the former has higher train efficiency, but lower mean average precisions(MAP). To improve the MAP of the proposed model, we investigate the result fusion mode to form multi-channel identification model, the best MAP reaches 0.9998. The number o…
IIT Mandi（曼迪印度理工学院）；University of Oxford（牛津大学）；Idiap Research Institute（伊迪亚普研究所）
In this work, we propose a supervised, convex representation based audio hashing framework for bird species classification. The proposed framework utilizes archetypal analysis, a matrix factorization technique, to obtain convex-sparse representations of a bird vocalization. These convex representations are hashed using Bloom filters with non-cryptographic hash functions to obtain compact binary codes, designated as conv-codes. The conv-codes extracted from the training examples are clustered using class-specific k-medoids clustering with Jaccard coefficient as the similarity metric. A hash tab…
Many approaches have been used in bird species classification from their sound in order to provide labels for the whole of a recording. However, a more precise classification of each bird vocalization would be of great importance to the use and management of sound archives and bird monitoring. In this work, we introduce a technique that using a two step process can first automatically detect all bird vocalizations and then, with the use of 'weakly' labelled recordings, classify them. Evaluations of our proposed method show that it achieves a correct classification of 61% when used in a synthet…
The deployment of an expert system running over a wireless acoustic sensors network made up of bioacoustic monitoring devices that recognise bird species from their sounds would enable the automation of many tasks of ecological value, including the analysis of bird population composition or the detection of endangered species in areas of environmental interest. Endowing these devices with accurate audio classification capabilities is possible thanks to the latest advances in artificial intelligence, among which deep learning techniques excel. However, a key issue to make bioacoustic devices af…
We present a robust classification approach for avian vocalization in complex and diverse soundscapes, achieving second place in the BirdCLEF2021 challenge. We illustrate how to make full use of pre-trained convolutional neural networks, by using an efficient modeling and training routine supplemented by novel augmentation methods. Thereby, we improve the generalization of weakly labeled crowd-sourced data to productive data collected by autonomous recording units. As such, we illustrate how to progress towards an accurate automated assessment of avian population which would enable global biod…
Cross-Dialect Bird Species Recognition with Dialect-Calibrated Augmentation
GLAM, Department of Computing, Imperial College London, UK（GLAM，计算系，帝国理工学院伦敦校区，英国）；CHI - Chair of Health Informatics, TUM University Hospital, Munich, Germany（健康信息学主席，技术大学慕尼黑医院，慕尼黑，德国）；MDSI – Munich Data Science Institute, Munich, Germany（慕尼黑数据科学研究所，慕尼黑，德国）；MCML – Munich Center for Machine Learning, Munich, Germany（慕尼黑机器学习中心，慕尼黑，德国）
This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizing the entire class dictionary of the problem at hand, that is rarely true as a habitat's species composition is only known up to a certain extent. Thus, the problem needs to be addressed by methodologies able to cope with non-stationarity. To this end, we propose a framework able to detect changes in the class dictionary and incorporate new classes on the fly. We design an one-shot learning architecture composed of a…
Machine Listening Lab, Centre for Digital Music (C4DM), Queen Mary Univ. of London, UK Centre d'Ecologie et des Sciences de la Conservation (CESCO), Mus\' e um National d'Histoire Naturelle, CNRS, Sorbonne Univ., Paris, France Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, Univ. de Montpellier, Univ. Paul-Val\' e ry Montpellier, France AGH Univ. of Science；Technology, Department of Mechanics；Vibroacoustics, Krak\' o w, Poland Univ. Toulon, Aix Marseille Univ., CNRS, LIS, DYNI team, SABIOD, Marseille, France
Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird pop…
MyGardenBird: A Machine-Learning-Ready Bird Sound Dataset for Twelve Common Malaysian Birds
Faculty of Computer Science and Information Technology, Universiti Malaya（马来大学计算机科学与信息技术学院）；Faculty of Electrical Engineering, Universiti Teknologi Malaysia（技术学院电气工程学院）
Birds are vital parts of ecosystems across the world and are an excellent measure of the quality of life on earth. Many bird species are endangered while others are already extinct. Ecological efforts in understanding and monitoring bird populations are important to conserve their habitat and species, but this mostly relies on manual methods in rough terrains. Recent advances in Machine Learning and Deep Learning have made automatic bird recognition in diverse environments possible. Birdcall recognition till now has been performed using convolutional neural networks. In this work, we try and u…
University of Pretoria（比勒陀利亚大学）；Dedan Kimathi University of Technology（德丹·基马蒂科技大学）
This paper is an investigation into aspects of an audio classification pipeline that will be appropriate for the monitoring of bird species on edges devices. These aspects include transfer learning, data augmentation and model optimization. The hope is that the resulting models will be good candidates to deploy on edge devices to monitor bird populations. Two classification approaches will be taken into consideration, one which explores the effectiveness of a traditional Deep Neural Network(DNN) and another that makes use of Convolutional layers.This study aims to contribute empirical evidence…
Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
Queen Mary University of London（伦敦玛丽女王大学）；University of Salford（索尔福德大学）；AGH University of Science and Technology（AGH科技大学）；University of Crete（克里特大学）；University of Toulon（土伦大学）
Institut Systématique, Évolution, Biodiversité (ISYEB), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles（系统学、进化与生物多样性研究所（ISYEB），法国国家自然历史博物馆，法国国家科学研究中心，索邦大学，巴黎高等研究实践学院，安第列斯大学）
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…
Generalization in birdsong classification: impact of transfer learning methods and dataset characteristics
Naturalis Biodiversity Center（自然生物多样性中心）；Xeno-Canto Foundation（异音基金会）；Landesbund fur Vogel- und Naturschutz（鸟类与自然保护州联合会）；Department of Cognitive Science and Artificial Intelligence, Tilburg University（蒂尔堡大学认知科学与人工智能系）
Transfer Learning with Pseudo Multi-Label Birdcall Classification for DS@GT BirdCLEF 2024
Georgia Institute of Technology（佐治亚理工学院）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
Freie Universität Berlin（柏林自由大学）；Berlin University of Applied Sciences（柏林应用科学大学）；Woks Audio GmbH（沃克斯音频有限公司）；Royal Holloway University of London（伦敦大学皇家霍洛威学院）；Einstein Center for Digital Future（数字未来爱因斯坦中心）；Inria（法国国家信息与自动化研究所）
Audio sound recognition and classification is used for many tasks and applications including human voice recognition, music recognition and audio tagging. In this paper we apply Mel Frequency Cepstral Coefficients (MFCC) in combination with a range of machine learning models to identify (Australian) birds from publicly available audio files of their birdsong. We present approaches used for data processing and augmentation and compare the results of various state of the art machine learning models. We achieve an overall accuracy of 91% for the top-5 birds from the 30 selected as the case study.…
SEABAD: A Tropical Bird Activity Detection Dataset for Passive Acoustic Monitoring
Faculty of Computer Science and Information Technology, Universiti Malaya（马来亚大学计算机科学与信息技术学院）；Faculty of Electrical Engineering, Universiti Teknologi Malaysia（马来西亚科技大学电气工程学院）
A Bird Song Detector for improving bird identification through Deep Learning: a case study from Doñana
University of Córdoba（科尔多瓦大学）；Estación Biológica de Doñana（多纳纳生物站）；University of Cádiz（卡迪斯大学）；Universidad Politécnica de Madrid（马德里理工大学）；University of Sevilla（塞维利亚大学）
Technological Educational Institute of Crete（克里特技术教育学院）
This work focuses on reliable detection and segmentation of bird vocalizations as recorded in the open field. 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. These tasks are tackled in this work, by suggesting two approaches: A) First, DenseNets are applied to weekly labeled data to infer the attention map of the dataset (i.e. Salience and CAM). We push further this idea by directing attention maps to the YOLO v2 Deepnet-based, detection framework to localize bird vocalizations. B)…
University of Würzburg（维尔茨堡大学）；Institute of Computer Science, University of Würzburg（维尔茨堡大学计算机科学研究所）
It is easier to hear birds than see them. However, they still play an essential role in nature and are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Deep Neural Networks allow us to process audio data to detect and classify birds. This technology can assist researchers in monitoring bird populations and biodiversity. We propose a sound detection and classification pipeline to analyze complex soundscape recordings and identify birdcalls in the background. Our method learns from weak labels and few data and acoustically recognizes the bird species.…
Biodiversity monitoring using audio recordings is achievable at a truly global scale via large-scale deployment of inexpensive, unattended recording stations or by large-scale crowdsourcing using recording and species recognition on mobile devices. The ability, however, to reliably identify vocalising animal species is limited by the fact that acoustic signatures of interest in such recordings are typically embedded in a diverse and complex acoustic background. To avoid the problems associated with modelling such backgrounds, we build generative models of bird sounds and use the concept of nov…
Birdsong often contains large amounts of rapid frequency modulation (FM). It is believed that the use or otherwise of FM is adaptive to the acoustic environment, and also that there are specific social uses of FM such as trills in aggressive territorial encounters. Yet temporal fine detail of FM is often absent or obscured in standard audio signal analysis methods such as Fourier analysis or linear prediction. Hence it is important to consider high resolution signal processing techniques for analysis of FM in bird vocalisations. If such methods can be applied at big data scales, this offers a …
One hour before sunrise, one can experience the dawn chorus where birds from different species sing together. In this scenario, high levels of polyphony, as in the number of overlapping sound sources, are prone to happen resulting in a complex acoustic outcome. Sound Event Detection (SED) tasks analyze acoustic scenarios in order to identify the occurring events and their respective temporal information. However, highly dense scenarios can be hard to process and have not been studied in depth. Here we show, using a Convolutional Recurrent Neural Network (CRNN), how birdsong polyphonic scenario…
Modern speech synthesis uses neural vocoders to model raw waveform samples directly. This increased versatility has expanded the scope of vocoders from speech to other domains, such as music. We address another interesting domain of bio-acoustics. We provide initial comparative analysis-resynthesis experiments of birdsong using traditional (WORLD) and two neural (WaveNet autoencoder, parallel WaveGAN) vocoders. Our subjective results indicate no difference in the three vocoders in terms of species discrimination (ABX test). Nonetheless, the WORLD vocoder samples were rated higher in terms of r…
Ultra-Compact CNN Architectures for Tropical Bird Audio Detection on Microcontrollers
Faculty of Computer Science and Information Technology, Universiti Malaya（马来西亚大学计算机科学与信息技术学院）；Faculty of Electrical Engineering, Universiti Teknologi Malaysia（马来西亚理工大学电气工程学院）
Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen eva…
University of Oxford（牛津大学）；University of Southampton（南安普顿大学）
We evaluated the effectiveness of an automated bird sound identification system in a situation that emulates a realistic, typical application. We trained classification algorithms on a crowd-sourced collection of bird audio recording data and restricted our training methods to be completely free of manual intervention. The approach is hence directly applicable to the analysis of multiple species collections, with labelling provided by crowd-sourced collection. We evaluated the performance of the bird sound recognition system on a realistic number of candidate classes, corresponding to real con…
Research Collaboratory for Advanced Intelligent Systems（高级智能系统研究合作中心）；Institute of Computer Science, University of the Philippines Los Baños（菲律宾大学洛斯巴诺斯分校计算机科学学院）
In this research endeavor, it was hypothesized that the sound produced by animals during their vocalizations can be used as identifiers of the animal breed or species even if they sound the same to unaided human ear. To test this hypothesis, three artificial neural networks (ANNs) were developed using bioacoustics properties as inputs for the respective automatic identification of 13 bird species, eight dog breeds, and 11 frog species. Recorded vocalizations of these animals were collected and processed using several known signal processing techniques to convert the respective sounds into comp…
ARIONet: An Advanced Self-supervised Contrastive Representation Network for Birdsong Classification and Future Frame Prediction
Department of Computer Science and Engineering, United International University（计算机科学与工程系，国际大学）；Faculty of Science and Technology, Charles Darwin University（科学与技术学院，查尔斯达尔文大学）；Faculty of Science and Information Technology, Charles Darwin University（科学与信息技术学院，查尔斯达尔文大学）
A strongly annotated passive acoustic dataset for tropical bird monitoring
Microsoft AI for Good Research Lab（微软AI for Good研究实验室）；Instituto de Investigación de Recursos Biológicos Alexander von Humboldt（亚历山大冯·洪堡生物资源研究所）；Center for Research and Formation in Artificial Intelligence（人工智能研究与培养中心）；Fundación Manacus（曼卡斯基金会）；Louisiana State University（路易斯安那州立大学）；Museum of Natural Sciences（自然博物馆）
Technological Educational Institute of Crete（克里特技术教育学院）
This work focuses on reliable detection of bird sound emissions as recorded in the open field. 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. Recordings in the wild can be very noisy due to the exposure of the microphones to a large number of audio sources originating from all distances and directions, the number and identity of which cannot be known a-priori. The co-existence of the target vocalizations with abiotic…
