Bird Species Recognition Research Articles

Bird Species Detection Using Deep Learning

Bird species classification plays a crucial role in various ecological and conservation endeavors. Leveraging deep learning techniques has shown promise in automating this task, offering the potential for efficient and accurate species identification. This paper presents a comprehensive study on the application of the YOLOv5 architecture, a state-of-the-art object detection framework, for bird species classification from images. We explore the effectiveness of YOLOv5 in comparison to traditional convolutional neural networks (CNNs) for bird species identification. Our study involves extensive experimentation with different configurations of YOLOv5, including variations in model size, training strategies, and dataset preprocessing techniques. We evaluate the performance of the YOLOv5-based classification framework on diverse datasets, ranging from publicly available bird image datasets to custom datasets collected through field observations. Additionally, we compare the classification accuracy of YOLOv5 with other popular CNN architectures, such as ResNet and EfficientNet, to assess its efficacy in bird species recognition tasks. Furthermore, we investigate the transferability of pre-trained YOLOv5 models to different bird species datasets and examine the robustness of the models to variations in image quality, background clutter, and occlusions.Our experimental results demonstrate that YOLOv5 offers competitive performance in bird species classification tasks, achieving high accuracy and efficiency. We discuss the strengths and limitations of using YOLOv5 for this application and provide insights into potential avenues for future research.

Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning

Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications.

On the role of audio frontends in bird species recognition

Automatic acoustic monitoring of bird populations and their diversity is in demand for conservation planning. This requirement and recent advances in deep learning have inspired sophisticated species recognizers. However, there are still open challenges in creating reliable monitoring systems of natural habitats. One of many open questions is whether predominantly used audio features like mel-filterbanks are appropriate for such analysis since their design follows human's perception of the sound, making them susceptible to discarding fine details from other animals' vocalization. Although research shows that different audio features work better for particular tasks and datasets, it is hard to attribute all advantages to input features since the experimental setups vary. A general solution is to design a learnable audio frontend to extract task-relevant features from raw waveform since it contains all the information in other audio features. The current paper thoroughly analyzes the role of such frontends in bird species recognition, which helped to evaluate the adequacy of traditional time-frequency representations (static frontends) in capturing the relevant information from bird vocalization. In particular, this work shows that the main performance gain in learnable audio frontends comes from the normalization and compression operations rather than the data-driven frequency selectivity and functional form of filters. We observed no significant discrepancy between the frequency bands of the learned and static frontends for bird vocalization. Although the performance of learnable frontends was much higher, we will show that adequate normalization and compression enhance the accuracy of traditional frontends by more than 16% to achieve comparable results for bird species recognition. Ablation studies of the frontends under different configurations and detailed analysis of noise robustness provide evidence for the conclusions, validate the use of mel-filterbanks and similar features in prior works, and provide guidelines for designing future species recognizers. The code is available at https://github.com/houtan-ghaffari/bird-frontends.

Recognition of bird species with birdsong records using machine learning methods.

The recognition of bird species through the analysis of their vocalizations is a crucial aspect of wildlife conservation and biodiversity monitoring. In this study, the acoustic features of Certhia americana, Certhia brachydactyla, and Certhia familiaris were calculated including the Acoustic complexity index (ACI), Acoustic diversity index (ADI), Acoustic evenness index (AEI), Bioacoustic index (BI), Median of the amplitude envelop (MA), and Normalized Difference Soundscape Index (NDSI). Three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were constructed. The results showed that the XGBoost model had the best performance among the three models, with the highest accuracy (0.8365) and the highest AUC (0.8871). This suggests that XGBoost is an effective tool for bird species recognition based on acoustic indices. The study provides a new approach to bird species recognition that utilizes sound data and acoustic characteristics.

Bird species recognition using transfer learning with a hybrid hyperparameter optimization scheme (HHOS)

The use of automatic bird species recognition methods reduces the burden on scientists, ornithologists, and bird watchers as these methods help identify birds with minimal human effort and intervention. The current study employs a transfer learning approach combined with a Hybrid hyperparameter Optimization Scheme (HHOS) to enhance the efficiency and accuracy of automatic bird species recognition. First, the weights of selected pre-trained deep learning models are downloaded from ImageNet, and a few new trainable layers are added at the top. Thereafter, the selected models are trained using HHOS, which strategically integrates both manual and random searches to achieve favorable results. The manual search relies on domain knowledge and experience to identify the best hyperparameter settings, thereby making the search space smaller and more focused. Random search tests various combinations of the hyperparameters identified in manual search and trains the selected models to achieve the maximum possible accuracy through multiple iterations. Experimental analysis revealed that the Fine-tuned EfficientNetB0 model exhibited superior performance, achieving an accuracy of 99.12%. In contrast, the performance of the ResNet18 model was disappointing with an accuracy of 93.24%, while other models outperformed it.

Bird Sound Identification System using Deep Learning

This paper identifies the model for classifying the bird sounds using the Convolutional Neural Network (CNN) algorithm. CNN are considered as one of the powerful toolkits developed in the machine learning and that have shown to be more efficient particularly in the field of image processing and sound recognition. The two-step verification process which will participating in the creation of datasets and neural network input. This system uses the collection of labelled and categorized recordings. Spectrograms are generated from the downloaded data which is used as the input for the neural network. The experiments are conducted and it will match with the different configurations, which including the number of bird species and Spectrograms. As a result, we can identify the bird species from its audio recordings. By identifying the bird species on a large scale, it provides a vital information about our ecosystems and biodiversity. The changes in the ecological environment can be reflected by the quantity and composition of bird species. It is one of the major challenges which leads to recognition of many bird species based on their audio. In modern years, the deep learning techniques namely convolutional neural networks (CNN) have played a major role in many areas of science and also successfully provided efficient methodologies, such as pattern recognition, object detection in images, and classification of patterns in audios and speech recognition.

Recognition of Bird Species of Yunnan Based on Improved ResNet18

Recognition of Bird Species of Yunnan Based on Improved ResNet18

Bird Species Recognition System with Fine-Tuned Model

A bird recognition system identifies bird species by combining computer vision and machine learning techniques to categorize different bird species with high accuracy. Moreover, the bird species recognition system represents a significant advance in animal protection and zoological research, especially for the rare and elusive bird species living in the jungle. This work focuses on an image-based system for bird species recognition. In bird species recognition, users input the bird images, and the system uses a deep learning model trained for optimal results in identifying different bird species from the images. We used fine-tuned deep learning models (Inception-V3 and EfficientNet-B4) to evaluate and determine which model can best perform image-based bird species recognition. Several unique datasets were used to evaluate and determine which model was best suited for image-based bird species recognition. These datasets consist of CUB -200-2011, Kaggle-510 bird species, 325 bird species, and a self-generated dataset (100 bird species from Malaysia). When applied to these four different datasets, the experimental results clearly show the advantage of fine-tuning the deep learning models. This study makes an important contribution to ornithology by providing a robust and trustworthy method for identifying and cataloging bird species, especially those that are rarely seen in the wild. Thus, the bird identification system is important for scientific research and animal welfare.

Bird species recognition using spiking neural network along with distance based fuzzy co-clustering

Bird species recognition using spiking neural network along with distance based fuzzy co-clustering

Cross-corpus open set bird species recognition by vocalization

In the wild, bird vocalizations of the same species across different populations may be different (e.g., so called dialect). Besides, the number of species is unknown in advance. These two facts make the task of bird species recognition based on vocalization a challenging one. This study treats this task as an open set recognition (OSR) cross-corpus scenario. We propose Instance Frequency Normalization (IFN) to remove instance-specific differences across different corpora. Furthermore, an x-vector feature extraction model integrated Time Delay Neural Network (TDNN) and Long Short-Term Memory (LSTM) are designed to better capture sequence information. Finally, the threshold-based Probabilistic Linear Discriminant Analysis (PLDA) is introduced to discriminate the extracted x-vector features to discover the unknown classes. When compared to the best results of the existing method, the average ACCs for the single-corpus and cross-corpus experiments are improved, implying that our method can provide a potential solution and improve performance for cross-corpus bird species recognition based on vocalization in open set condition.

A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments

In this paper, we propose a deep learning approach for visual recognition of bird species in noisy environments. Bird species recognition has been a challenging task due to the high variation in bird appearances and the presence of noise and clutter in natural environments. Our approach utilizes a deep convolutional neural network (CNN) to learn discriminative features from bird images and classify them into different species. We also incorporate data augmentation techniques to increase the diversity of the training data and improve the robustness of the model. To address the issue of noisy environments, we introduce a novel noise-robust loss function that penalizes the model for incorrect predictions caused by noise. We evaluate our approach on a dataset of bird images collected from diverse environments and compare it with state-of-the-art methods. Our results demonstrate that our approach achieves superior performance in both clean and noisy environments, highlighting the effectiveness of our noise-robust loss function. Our approach has the potential to be applied in real-world scenarios for bird species recognition and conservation.

Development of species recognition models using Google teachable machine on shorebirds and waterbirds

Species identification is an essential ability in every conservation initiative. An efficient and robust computer vision method was attested with an available online tool with Google's Teachable Machine. This pilot study on developing a species recognition app was to create and evaluate the usability and accuracy of using Teachable Machine for species identification at Teluk Air Tawar, Kuala Muda (TAT-KM), Malaysia. The accuracy of the created models was evaluated and compared with training images based on the web-mining (Google Images Repository) compared to actual photos taken at the same site. Model A (Google Image) had an average accuracy of 55.30%, while Model B (actual photos) was 99.42%. Regarding success rate at accuracy over 77%, 27 out of 49 test images (55.10%) were reported in Model A, while Model B had a 100% success rate. This approach can replace traditional methods of bird species recognition to handle large amounts of data.

Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning

Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning

Recognition of Aras Bird Species From Their Voices With Deep Learning Methods

Bu çalışmada Iğdır Aras Nehri Kuş Cenneti'nde sıklıkla görülen kuş türlerinin seslerinden tanınması üzerinde durulmuştur. Bu amaçla derin öğrenme yöntemleri kullanılmıştır. Biyolojik çeşitliliğin incelenmesi ve analiz edilmesi için akustik gözetleme çalışmaları yapılmaktadır. Bu iş için pasif dinleyici/kaydedici adındaki aygıtlar kullanılmaktadır. Genel olarak bu kaydedici aygıtlarla toplanan ham ses kayıtlarının üzerinde çeşitli analizler gerçekleştirilir. Bu çalışmada, kuşlardan elde edilen ham ses kayıtları tarafımızca geliştirilen yöntemlerle işlenmiş ve daha sonra derin öğrenme mimarileriyle kuş türleri sınıflandırılmıştır. Sınıflandırma çalışmaları, Aras Kuş Cenneti’nde çokça görülen 22 kuş türü üzerinde yapılmıştır. Ses kayıtları 10 saniyelik klipler haline getirilmiş daha sonra bunlar birer saniyelik log mel spektrogramlara çevrilmiştir. Sınıflandırma yöntemi olarak derin öğrenme mimarilerinden Evrişimsel Sinir Ağları (CNN) ve Uzun Kısa-Dönemli Bellek Sinir Ağları (LSTM) kullanılmıştır. Ayrıca bu iki modelin yanında Öğrenme Aktarımı yöntemi de kullanılmıştır. Öğrenme aktarımı için kullanılan ön-eğitimli evrişimsel sinir ağlarından VGGish ve YAMNet modelleriyle seslerin yüksek seviyeli öznitelik vektörleri çıkarılmıştır. Çıkarılan bu vektörler sınıflandırıcıların giriş katmanlarını oluşturmuştur. Yapılan deneylerle dört farklı mimarinin ses kayıtları üzerindeki doğruluk oranları ve F1 skorları bulunmuştur. Buna göre en yüksek doğruluk oranı (acc) ve F1 skoru sırasıyla %94.2 ve %92.8 ile VGGish modelinin kullanıldığı sınıflandırıcıyla elde edilmiştir.

Convolutional neural network bird’s classification using north American bird images

In general, image classification is more like to classify objects with large categories, where these objects have a low level of similarity that is relatively rare. Birds image classification is a tough image dataset annotated with many bird species. It may be a challenging issue as numerous of the species of birds have degree of visual closeness. Bird species recognition can be challenging for people, let alone computer vision calculations. To analyze the image, this paper resizes the image into 224224 pixels. We use the Convolutional Neural network (CNN) approach and add the structure of MobileNetV2, EfficientNetB0, EfficientNetB3 and the weight of the network that has previously trained using ImageNet. This paper attempts to analyze the comparative results from using MobileNetV2, EfficientNetB0 and EfficientNetB3 architecture. The F1 weighted average score MobileNetV2 is 75%, EfficientNet B0 is 81% and EfficientNet B3 is 81%. This result shows that some models are completely unable to predict some classes of birds, but other models can recognize them. This can happen because of differences of the operation in each CNN architecture parameter. These models complement each other.

Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution

Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast learning speed and good generalization ability, which is widely used in classification and recognition problems. Input layer weights and hidden layer thresholds are two key factors affecting ELM performance. As one of swarm intelligence optimization methods, differential evolution (DE) can be used to optimize the parameters of ELM. In order to enhance the diversity, convergence speed and global search ability of the DE population, and improve the accuracy and stability of the classification model, this paper proposes a multi-strategy differential evolution method (M-SDE) to optimize the parameters of the ELM. And the differential MFCC feature parameters, extracted from birdsongs, are applied to build classification models of M-SDE_ELM and an ensemble M-SDE_EnELM with optimized ELM for bird species recognition. In the experiments, the ELM models optimized by the swarm intelligence algorithms PSO and GOA are compared and analyzed by hypothesis tests with the M-SDE_ELM and M-SDE_EnELM. Results show that the M-SDE_ELM and M-SDE_EnELM can achieve a classification accuracy of 86.70% and 89.05% in the classification of nine species of birds respectively, and the recognition effect and stability of the M-SDE_EnELM model outperform other models.

Open set classification strategies for long-term environmental field recordings for bird species recognition.

Deep learning is one established tool for carrying out classification tasks on complex, multi-dimensional data. Since audio recordings contain a frequency and temporal component, long-term monitoring of bioacoustics recordings is made more feasible with these computational frameworks. Unfortunately, these neural networks are rarely designed for the task of open set classification in which examples belonging to the training classes must not only be correctly classified but also crucially separated from any spurious or unknown classes. To combat this reliance on closed set classifiers which are singularly inappropriate for monitoring applications in which many non-relevant sounds are likely to be encountered, the performance of several open set classification frameworks is compared on environmental audio datasets recorded and published within this work, containing both biological and anthropogenic sounds. The inference-based open set classification techniques include prediction score thresholding, distance-based thresholding, and OpenMax. Each open set classification technique is evaluated under multi-, single-, and cross-corpus scenarios for two different types of unknown data, configured to highlight common challenges inherent to real-world classification tasks. The performance of each method is highly dependent upon the degree of similarity between the training, testing, and unknown domain.

Bird Species Identification using Audio Signal Processing and Neural Networks

Abstract: In this work, automatic bird species recognition systems were developed, and their identification methods were investigated. Automatically identifying bird calls without physical intervention has been a large and tedious endeavor for major studies in various subfields of taxonomy and other ornithology. This task uses a two-step identification process. In the first phase, an ideal dataset was created containing all recordings of different bird species. Next, the sound clip was subjected to various sound pre-processing techniques such as pre-emphasis, framing, silence removal, and reconstruction. A spectrogram was generated for each reconstructed sound clip. The second step used a neural network given with the spectrogram as input. A convolutional neural network (CNN) classifies sound clips and recognizes bird species based on input characteristics. In the above system, a real-time implementation model was also designed and executed. Keywords: Bird species identification, bird sound, sound pre-processing techniques, Convolutional Neural Network, Spectrograms

AI based Birds Sound Classification Using Convolutional Neural Networks

To classify and recognize the sounds which are produced by the birds which are used for the identification of species of the bird. The identification of bird species in captured audio files will be a transformational method for scholars, wildlife biologists and birders. In latest years, artificial neural networks have dramatically increased the detection efficiency of bird species recognition using machine learning systems. There is a lot of study in audio recognition using machine learning. This work may help for the easy identification of birds living in a locality and studying of birds’ migration.

Sound-spectrogram based automatic bird species recognition using MLP classifier

Bioacoustics play a major role in the field of ornithology, ecology, animal behaviour study, habitat monitoring, species conservation and design of deterrent system. This work focusses on the design and implementation and performance evaluation of an automatic, more efficient and flexible bird sound based recognition system for classifying eight species of popular Eurasian birds the standard online annotated databases. A dedicated virtual instrument tool with effective GUI is developed that acquires, pre-processes the sound samples and generates statistically evaluated short term Fourier transform spectrogram based feature matrix, suited for characterization of vocalization patterns of bird species. Using the well-labelled feature data of sound records, multi-layer perceptron artificial neural network (MLP-NN) classifier model is designed, trained, tested and optimized using feedforward-backpropagation supervised learning algorithm. Various experiments, following a systematic approach, are conducted to optimize the structure of MLP with respect of number of neurons in the hidden layer, epochs and learning rate for attaining enhanced recognition accuracy (96.1%), recall (82.6%) and precision (84.5%). The performance of the optimal model is also analysed in terms of recognition capabilities of individual bird species that indicate promising results. A few of the birds are recognized accurately and precisely when present as compared to the others. The tendency of the model to wrongly identify or miss bird species also remained low. The model performance over the unseen dataset also remained satisfactory with cross-validation classification accuracy of 81.4%. The system being scalable, can easily be reused in future to retrain the model over the large set of sound samples from real world recordings with improved acoustic features for achieving very high classification accuracy and reliability.