Bird Sounds Research Articles

Investigation of Bird Sound Transformer Modeling and Recognition

Birds play a pivotal role in ecosystem and biodiversity research, and accurate bird identification contributes to the monitoring of biodiversity, understanding of ecosystem functionality, and development of effective conservation strategies. Current methods for bird sound recognition often involve processing bird songs into various acoustic features or fusion features for identification, which can result in information loss and complicate the recognition process. At the same time, the recognition method based on raw bird audio has not received widespread attention. Therefore, this study proposes a bird sound recognition method that utilizes multiple one-dimensional convolutional neural networks to directly learn feature representations from raw audio data, simplifying the feature extraction process. We also apply positional embedding convolution and multiple Transformer modules to enhance feature processing and improve accuracy. Additionally, we introduce a trainable weight array to control the importance of each Transformer module for better generalization of the model. Experimental results demonstrate our model’s effectiveness, with an accuracy rate of 99.58% for the public dataset Birds_data, as well as 98.77% for the Birdsonund1 dataset, and 99.03% for the UrbanSound8K environment sound dataset.

Advanced emotion analysis: harnessing facial image processing and speech recognition through deep learning

The human face hides many secrets and is one of the most expressive human features. Human faces even contain hidden information about a person's personality. Considering the fundamental role of the human face, it is necessary to prepare appropriate deep-learning solutions that analyze human face data. This technology is becoming increasingly common in many industries, such as online retail, advertising testing, virtual makeovers, etc. For example, facial analysis technology now allows online shoppers to virtually apply makeup and try on jewelry or new glasses to get an accurate picture of what these products will look like. The human sense of hearing is a treasure trove of information about the current environment and the location and properties of sound-producing objects. For instance, we effortlessly absorb the sounds of birds singing outside the window, traffic passing in the distance, or the lyrics of a song on the radio. The human auditory system can process the intricate mix of sounds reaching our ears and create high-level abstractions of the environment by analyzing and grouping measured sensory signals. The process of obtaining segregation and identifying sources of a received complex acoustic signal, known as sound scene analysis, is a domain where the power of deep learning shines. The machine implementation of this functionality (separation and classification of sound sources) is pivotal in applications such as speech recognition in noise, automatic music transcription, searching and retrieving multimedia data, or recognizing emotions in statements.

SPECIES STRUCTURE AND NUMBER OF BIRDS OF THE LOWER TYLIGUL ESTUARY IN THE SPRING OF 2019–2023

The aim of the work is to assess the dynamic characteristics of hydrophilic birds in the spring period of 2019–2023. Methods. In the open area, bird counts were carried out from a car with numerous stops to inspect territories and water areas using 10×, 12× binoculars and a 30–60× telescope. Observations were conducted on foot in areas with tree-shrub vegetation and along small water bodies. Registration of species was carried out both by visual observations and by the sounds of birds. Main results. 99 bird species of 13 orders were registered. Representatives of theorders Anseriformes, Gruiformes, Charadriiformes and Passeriformes dominated. Conclusions. Within the relatively little-changed natural complexes and in the adjacent territories and water areas of the region, relative stability of species diversity of birds and their quantitative characteristics has been observed in recent years. The population dynamics observed in some ecological groups of birds do not go beyond the normal fluctuations that are naturally inherent to them. Signs of an increase in the number of some species within the studied region in recent years can be associated with an increase in the level of danger in the eastern part of the Azov-Black Sea region in connection with military events.

Meta-Embedded Clustering (MEC): A new method for improving clustering quality in unlabeled bird sound datasets

In recent years, ecoacoustics has offered an alternative to traditional biodiversity monitoring techniques with the development of passive acoustic monitoring (PAM) systems allowing, among others, to detect and identify species that are difficult to detect by human observers, automatically. PAM systems typically generate large audio datasets, but using these monitoring techniques to infer ecologically meaningful information remains challenging. In most cases, several thousand hours of recordings need to be manually labeled by experts limiting the operability of the systems. Based on recent developments of meta-learning algorithms and unsupervised learning techniques, we propose here Meta-Embedded Clustering (MEC), a new method with high potential for improving clustering quality in unlabeled bird sound datasets. MEC method is organized in two main steps, with: (a) fine-tuning of a pretrained convolutional neural network (CNN) backbone with different meta-learning algorithms using pseudo-labeled data, and (b) clustering of manually-labeled bird sounds in the latent space based on vector embeddings extracted from the fine-tuned CNN. The MEC method significantly enhanced average clustering performance from less than 1% to more than 80%, greatly outperforming the traditional approach of relying solely on CNN features extracted from a general neotropical audio database. However, this enhanced performance came with the cost of excluding a portion of the data categorized as noise. By improving the quality of clustering in unlabeled bird sound datasets, the MEC method should facilitate the work of ecoacousticians in managing acoustic units of bird song/call clustered according to their similarities, and in identifying potential clusters of species undetected using traditional approaches.

Bird Sound Classification : Leveraging Deep Learning for Species Identification

Birds are meaningful to a wide audience including the public. They live in almost every type of environment and in almost every niche (place or role) within those environments. The monitoring of species diversity and migration is important for almost all conservation efforts. The analysis of long-term audio data is vital to support those efforts but relies on complex algorithms that need to adapt to changing environmental conditions. Convolutional neural networks (CNNs) are powerful toolkits of machine learning that have proven efficient in the field of image processing and sound recognition. In this paper, a CNN system classifying bird sounds is presented and tested through different configurations and hyperparameters. The MobileNet pre-trained CNN model is finetuned using a dataset acquired from the Xeno-canto bird song sharing portal, which provides a large collection of labeled and categorized recordings. Spectrograms generated from the downloaded data represent the input of the neural network. The attached experiments compare various configurations including the number of classes (bird species) and the color scheme of the spectrograms. Results suggest that choosing a color map in line with the images the network has been pre-trained with provides a measurable advantage. The presented system is viable only for a low number of classes.

The City Is the Club Is the City

Blurring the lines between inside and outside, civic and social, The City Is the Club Is the City is a spectrogram film that asks, “What if the club and the city were the same?” This piece was originally conceived of and created as a part of Tradewinds, an exhibition approaching DC’s go-go music club culture from dual angles of photography and soundscape. Rooted in collaborative research, the exhibition is the culmination of the Nexus Residency wherein photographer Larry Cook and ethnomusicologist Allie Martin worked in tandem to meld their practices, presenting a unified body of new works. Drawing on soundscape recordings created at the iconic corner of 7th Street and Florida Avenue NW, the film features a series of sonic vignettes that blur the lines between inside and outside, between varied publics. Utilizing traffic, club noise, go-go music, bird sounds, and other sonic elements, these vignettes bring background noise to the fore, creating the feeling of being suspended in a doorway where the club is the city, and the city is the club. As each vignette is built from multiple layers of sound, this accompanying essay peels back some of these layers, thinking through what sounds like the city and what sounds like the club, and the benefits to listening to both of these imagined spaces as if they were one. As Washington, DC, continues to gentrify, listening intersectionally in this way is imperative to understanding the intricacies of Black sound in the city.

Modernization in Environmental Change in the Novel “Baitun Nakhil” by Tarek Eltayeb: Greg Garrard’s Ecocritical Study

Abstract. Modernization had a significant impact on environmental change. This change is described in the novel “Baitun Nakhil” and studied by using ecocritical theory. Ecocriticism is a way to describe the relationship between humans and nature in all cultural environments. This study aims to identify ecological narratives related to environmental change as the modernization impact in the novel “Baitun Nakhil” by Tarek Eltayeb based on Garrard’s ecocritical perspective. This study is qualitative descriptive. The data used are narratives and dialogue in the novel “Baitun Nakhil”. Data were collected by reading and note-taking techniques. Data were validated by increasing persistence, triangulation, and discussion. Data were analyzed using the Miles and Huberman method, which consists of three stages: data reduction, data presentation, and conclusion. The results of the study are the discovery of environmental change as a modernization impact in several scopes according to Garrard as follows: 1) pastoral narratives in the form of spatial and temporal distinction; 2) pollution caused by cigarettes and sirens; 3) wilderness as in mountains and forest; 4) natural disasters in the form of a snowstorm; 5) dwelling adopts the georgic concept as in Wad Al-Kababish; 6) relation between animals and human as shown by the treatment of animals and imitation of bird sounds; and 7) earth changes initiated by the plastic usage and the construction of greenhouse as conservation efforts. This study proves that modernization destroys nature due to the massive exploitation of natural resources. Conversely, modernization makes it easier to campaign and conserve nature with its sophisticated technology. Keywords: Ecocriticism, Environmental Change, Greg Garrard, Modernization, Novel

Automatic Bird Sound Source Separation Based on Passive Acoustic Devices in Wild Environment

Automatic Bird Sound Source Separation Based on Passive Acoustic Devices in Wild Environment

Techniques and Resources for Teaching and Learning Bird Sounds

Techniques and Resources for Teaching and Learning Bird Sounds

The incidence of bird sounds, and other categories of non-focal sounds, confound the relationships between acoustic indices and bird species richness in southern China

The analysis of audio recordings through acoustic indices has been proposed as an efficient way to measure and monitor biodiversity, given the assumption that higher levels of biodiversity produce more rapidly-changing and complex sound. However, in previous work in south China, we have found only moderate correlations between the acoustic indices and bird species richness, when analyzing recordings that were made at the same time as conducting point counts of birds. Here, we extended that work in three study regions in Guangxi Province, making observations both inside relatively undisturbed forest reserves and in the surrounding agricultural lands. We found that of 42 correlations between bird species richness and the acoustic indices (seven commonly used acoustic indices [ACI, ADI, AEI, AR, BIO, H, NDSI] each calculated in six habitat/region combinations) only seven had even a moderate relationship (i.e., |r| ≥ 0.20). To understand this paucity of relationships, we listened to a subsample of the recordings, scoring them for various kinds of biophony (incidence of bird and insect sounds), anthropophony (incidence of observer-produced sounds, sounds made by other humans, and sounds made by machines) and geophony (primarily wind, occasionally water). These analyses found that insects were positively related to the acoustic indices, especially in forests. Moreover, anthropophony and geophony, were, in general, negatively correlated to the indices. The incidence of bird vocalizations also had consistent and strong correlations to the residuals of the models relating the acoustic indices to bird species richness, demonstrating that the indices are clearly sensitive to the amount of bird sounds, but that is not necessarily strongly correlated to bird diversity. Multivariate models confirmed that the amounts of different sound categories were more influential on the acoustic indices than bird species richness. Our results demonstrate how competing noises, in both relatively undisturbed environments and heavily modified ones, can confound relationships between the acoustic indices and biodiversity. We discuss possible remedial steps for using acoustic indices in noisy soundscapes, including tailoring the selection of indices and frequency bands for the objectives of the project, or, alternatively, using a big-data approach that combines multiple indices.

Automatic bioacoustics noise reduction method based on a deep feature loss network

Acoustic sensors that collect acoustic data over extended periods and broad ranges are widely used in bioacoustics monitoring. However, in open environments, acoustic data collected using acoustic sensors can be subject to interference from various real-world noises, thereby influencing the subsequent analysis and processing of bioacoustic data. Existing bioacoustic noise reduction methods are limited in their application because of their low efficiency, unsuitability for non-stationary noise, generally unimproved signal-to-noise ratio (SNR) efficacy, and considerable amounts of residual noise. These limitations hinder the effective processing of recorded signals for which extraneous noise overlaps with bird vocalizations. In this study, we propose a bioacoustic noise reduction method based on a deep feature loss network for bird sounds. The method has a rapid denoising speed and can more effectively remove background noise from field recording signals without distorting the bird acoustic spectrum. The denoising effects of the proposed method were compared with those of a speech enhancement generative adversarial network, web real-time communications denoising, and other noise reduction methods. The denoising ability of these methods for different noises was evaluated using spectrograms and objective evaluation measures such as the SNR and perceptual evaluation of speech quality (PESQ). The experimental results revealed that our proposed noise reduction method can obtain higher SNRs and PESQ scores than other noise reduction methods, with the SNR increasing by up to 35.83 dB following denoising.

Anthropogenic noise and habitat structure shaping dominant frequency of bird sounds along urban gradients

The shifts of bird song frequencies in urbanized areas provide a unique system to understand avian acoustic responses to urbanization. Using passive acoustic monitoring and automatic bird sound recognition technology, we explored the frequency variations of six common urban bird species and their associations with habitat structures. Our results demonstrated that bird song frequencies in urban areas were significantly higher than those in peri-urban and rural areas. Anthropogenic noise and habitat structure were identified as crucial factors shaping the acoustic space for birds. We found that noise, urbanization, and open understory spaces are factors contributing to the increase in the dominant frequency of bird sounds. However, habitat variables such as vegetation density and tree height can potentially slow down this upward trend. These findings offer essential insights into the behavioral response of birds in a variety of urban forest habitats, with implications for urban ecosystem management and habitat restoration.

The need for proper archiving and referencing of sound recordings in taxonomic studies of birds

Abstract A survey of recent taxonomic studies of birds that included acoustic trait analyses reveals that most studies have not archived the sound recordings that support their conclusions, despite the current availability of online, publicly available collections of bird sounds. In addition, bird sound recordings have often been cited without unique accession numbers that permit unambiguous sample identification and in considerably less detail than other types of samples, such as museum specimens or genetic samples. Both this lack of data openness and the way acoustic samples have been cited undermine the methodological rigor that otherwise characterizes many of these studies, and much invaluable biological data are likely to be lost over time if bird sound recordings are not archived in long-term collections. I suggest that these problems can be easily addressed by embracing the open data movement and adopting some best practices that are widely used in other fields. Just as study skins and DNA sequences are required to be deposited in publicly available collections such as natural history museums and the GenBank, respectively, sound recordings used in taxonomic studies with acoustic trait analyses should be archived in publicly available collections as a condition for publication of associated results. Authors of taxonomic studies involving sounds should archive their sound recordings and provide unique accession numbers for sound recordings examined, and journals and reviewers should ensure that authors have done so. By embracing the open data movement, research studying avian acoustic signals is expected to become more transparent, reproducible, and useful.

Can urban forests provide acoustic refuges for birds? Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity

As a crucial component of terrestrial ecosystems, urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic spaces. Previous studies note that vegetation structure is a key factor influencing bird sounds in urban forests; hence, adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their songs. However, it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic noise. It was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs, leading to a possible reshaping of the acoustic niches of forests, and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird vocalization. Passive acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises, and sounds were classified into three acoustic scenes (bird sounds, human sounds, and bird-human sounds) to determine interconnections between bird sounds, anthropogenic noise, and vegetation structure. Anthropogenic noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds, and vegetation structures related to volume (trunk volume and branch volume) and density (number of branches and leaf area index) significantly impact the diversity of bird sounds. Our findings indicate that the response to low and high frequency signals to vegetation structure is distinct. By clarifying this relationship, our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise.

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.

Common Bird Sound Recognition at Vietnam Based on CNN

This article about developing a software extracting bird sound from a website [13], that has sounds of different bird species in Vietnam, explores the CNN model to develop a bird sound recognition system. The process includes conducting methodological experiments on self-collected datasets, providing assessments based on obtained results and building a bird sound recognition application.

Identification of Birds' Voices Using Convolutional Neural Networks Based on Stft and Mel Spectrogram

Threats to the climate and global changes in ecological processes remain an urgent problem throughout the world. Therefore, it is important to constantly monitor these changes, in particular, using non-standard approaches. This task can be implemented on the basis of research on bird migration information. One of the effective methods of studying bird migration is the auditory method, which needs improvement. That is why building a model based on machine learning methods that will help to accurately identify the presence of bird voices in an audio file for the purpose of studying bird migrations from a given area is an urgent problem. This paper examines ways of building a machine learning model based on the analysis of spectrograms, which will help to accurately identify the presence of bird voices in an audio file for the purpose of studying the migration of birds in a certain area. The research involves the collection and analysis of audio files that can be used to identify characteristics that will identify the sound of the files as birdsong or the absence of sound in the file. The use of the CNN model for the classification of the presence of bird voices in an audio file is demonstrated. Special attention is paid to the effectiveness and accuracy of the CNN model in the classification of sounds in audio files, which allows you to compare and choose the best classifier for a given type of file and model. Analysis of the effectiveness and accuracy of the CNN model in the classification of sounds in audio files showed that the use of Mel-spectrograms is better than the use of STFT-spectrograms for studying the classification of the presence of bird sounds in the environment. The classification accuracy of the model trained on the basis of Mel spectrograms was 72 %, which is 8 % better than the accuracy of the model trained on STFT spectrograms.

CicadaNet: Deep learning based automatic cicada chorus filtering for improved long-term bird monitoring

Passive acoustic monitoring has been an effective tool for bird sound analysis. However, bird sounds often include cicada noise, which is an obstacle for investigating bird sounds. For example, cicada noise can result in large deviations of acoustic index, which will lead to the mismonitoring of species richness trends. Therefore, there is a critical need to filter cicada noise for helping bird sound analysis. We develop a novel end-to-end deep learning model, named CicadaNet for filtering cicada chorus from recordings containing bird sound. CicadaNet utilizes a convolutional encoder-decoder network to encode and decode acoustic features and a conformer module for global and local sequence modeling. We build a clean bird sound dataset and collect a large amount of real cicada noise data for model evaluation. We compare CicadaNet with current state-of-the-art deep denoising models and traditional denoising algorithms. Experimental results show that CicadaNet achieves the best denoising performance (SegSNR is improved by 9.59 dB and SI-SNR is improved by 20.08 dB when the noisy SNR = 0 dB). Meanwhile, CicadaNet achieves good performance for the real-time denoising of cicada noise. Furthermore, CicadaNet achieves bird species-independent noise reduction. We evaluate the effectiveness of CicadaNet for bird diversity survey. CicadaNet achieves the best performance, which can effectively eliminate the deviation caused by cicada noise to the acoustic index. CicadaNet can be easily extended to the cancellation of other environmental noise, and we propose it for the acoustic denoising of other vocalizing animals.

Soundscapes of two rural communities in Papua New Guinea

This article presents an analysis of the soundscapes in two rural communities in northwestern Papua New Guinea where the endangered languages Srenge and Walman are spoken. These languages have no written tradition and therefore are not publicly displayed in signage. In the last few years, the discipline of linguistic landscape studies has fostered the study of aspects of the visual semiotics of public signs together with aspects of other semiotic modalities including interactional semiotics and auditory semiotics. By discussing the sounds of instruments, conch shells, place names, recitations, and messenger birds in areas where Srenge, Walman, and other languages are spoken, the aim is twofold. The first goal is to fill a gap in the study of semiotic systems that play a role in linguistic landscapes by focusing on a rural area and on auditory semiotics. The second goal is to document anthropological knowledge in an area that is extremely rich culturally and linguistically but where language attrition and cultural loss have been greatly accelerated in the last few decades.

Acoustic bird species classification under low SNR and small-scale dataset conditions

Bird species classification based on acoustic monitoring has attracted increasing attention. Two common methods, deep learning and feature extraction-based, are widely applied for bird sound classification. Although deep learning based methods have shown great success, they usually require an enormous amount of labeled data to train models, which could make these approaches prohibitive since collecting enough labeled samples of various species, especially rare birds, is a difficult task. On the other hand, feature extraction based methods are less dependent on the number of samples and have achieved remarkable classification performance for small-scale datasets, however they prove susceptible to signal-to-noise ratio (SNR). To address this problem, a new method is proposed for acoustic bird species classification in this work, which is well suited to low SNR and small-scale dataset conditions. Firstly, an adaptive threshold scheme based on the constant false alarm rate (CFAR) criterion is designed to more effectively detect potential bird sound segments in field recordings. Then, the local wavelet acoustic pattern (LWAP) and Mel-frequency cepstral coefficients (MFCC) features are extracted, which are further processed using improved vector of locally aggregated descriptors (VLAD) encoding. Finally, encoded LWAP and MFCC features are fused with dimensionality reduction and a classifier with weighted distance is used for classification. Corresponding feature space analysis upon feature fusion is also provided demonstrating explicit improvement in bird sounds discrimination under low SNR conditions. Experimental results on the dataset consisting of 11 bird species under low SNR and small-scale conditions reveal that the proposed method achieves considerable improvement in classification performance as compared with other feature extraction based methods. Meanwhile, our approach also outperforms state-of-the-art deep learning based methods.