Birdsong Research Articles

An Analysis of the Dawn Vocalisation of a Male Grey Shrikethrush (Colluricincla harmonica)

ABSTRACT The vocalisations of the Grey Shrikethrush are loud, clear, pleasing to the ear, and a stand-out feature of the bird song in areas in which they occur. However, data on these vocalisations in the literature are sparse, as are any attempts at either quantitative or qualitative analysis. Consequently, almost nothing is known about the character of this very obvious song. We made eight recordings of the dawn song of a male individual at the start of the breeding season on the south-east coast of Australia, in spotted gum forest, 350 km south of Sydney. The song comprised discreet groups of notes which we call syllables. The syllables are sung about every 5-10 seconds, and the individual produced 20 different syllables over the course of the eight recordings. Various analyses revealed that new syllables were added gradually over the recording period, and reached a maximum number at the sixth recording, nine days after the first recording. The frequency of use of particular syllables varied markedly, it was common for some syllables to be used as repeats, particular heterogeneous syllable couplets were commonly used as alternations, and the sequence of syllables could be remarkably similar between different recordings. We suggest a way forward to determine whether these patterns are a common feature of the vocalisations of this species.

Estimating Mutual Information for Spike Trains: A Bird Song Example.

Zebra finches are a model animal used in the study of audition. They are adept at recognizing zebra finch songs, and the neural pathway involved in song recognition is well studied. Here, this example is used to illustrate the estimation of mutual information between stimuli and responses using a Kozachenko-Leonenko estimator. The challenge in calculating mutual information for spike trains is that there are no obvious coordinates for the data. The Kozachenko-Leonenko estimator does not require coordinates; it relies only on the distance between data points. In the case of bird songs, estimating the mutual information demonstrates that the information content of spiking does not diminish as the song progresses.

Subjective salience and the effects of familiarity with birdsongs and insect songs in noise environments

In Japan, birdsong is used to communicate the position of stairwells to visually impaired people at train stations. However, more than 40% of visually impaired people reported that such sounds were difficult to identify. Train companies seek to present sounds that are loud enough to be detected, but not loud enough to cause annoyance in noise environments. In this study, we examined the salience of different types of bird and insect songs in noise environments. We evaluated the subjective saliences of bird and insect songs with equal sound pressure levels using paired comparison methods. A total of 25 participants evaluated six types of bird and insect songs under three noise conditions. The relationships among subjective salience, physical parameters, and familiarity were examined. The results indicated that the sounds of Horornis diphone and Meimuna opalifera were the most salient types of birdsongs and insect songs, respectively, in noise environments. The degree of temporal and spectral change, binaural similarity, pitch, pitch strength, and familiarity were significant predictors of the subjective salience of birdsongs and insect songs in noise environments.This study contributes to enhance the safety of visually challenged people and to create a more attractive sound environment.

Deep learning bird song recognition based on MFF-ScSEnet

Bird diversity plays an important role in ecological balance, and bird song identification is of great practical significance. The spectrum generated by feature extraction shows good performance on classification. However, the information extracted by the filter in the process of spectrogram generation can cause information loss, which limits the learning ability of birdsong recognition. This study proposes a feature fusion network (MFF-ScSEnet) to solve this problem. The audios of the birdsong extracted the Mel-spectrogram with low-frequency feature advantage by the Mel-filter, and the Sinc-spectrogram with timbral feature advantage by the Sincnet-filter, respectively, and perform the early fusion strategy. The ScSEnet attention module is introduced into the backbone network ResNet18 to enhance the sound ripple information of the spectrogram, reduce the influence of spectrogram noise information on the recognition and improve the recognition performance of the network. Based on the feature fusion network MFF-ScSEnet in this paper, the accuracy of the experimental results on the self-built birdsong dataset (Huabei_dataset), the public datasets of Urbansound8K and Birdsdata reached 96.28%, 98.34%, and 96.66%, respectively. The results indicated that the method proposed in this paper is superior to the recent and latest birdsong recognition method.

Improving deep learning acoustic classifiers with contextual information for wildlife monitoring

Bioacoustics, the exploration of animal vocalizations and natural soundscapes, has emerged as a valuable tool for studying species within their habitats, particularly those that are challenging to observe. This approach has broadened the horizons of biodiversity assessment and ecological research. However, monitoring wildlife with acoustic recorders produces large volumes of data that can be labor-intensive to analyze. Deep learning has recently transformed many computational disciplines by enabling the automated processing of large and complex datasets and has gained attention within the bioacoustics community. Despite the revolutionary impact of deep learning on acoustic detection and classification, attaining both high detection accuracy and low false positive rates in bioacoustics remains a significant challenge. An intriguing yet unexplored avenue for enhancing deep learning in bioacoustics involves the utilization of contextual information, such as time and location, to discern animal vocalizations within acoustic recordings. As a first case study, a multi-branch Convolutional Neural Network (CNN) was developed to classify 22 different bird songs using spectrograms as a first input, and spatial metadata as a secondary input. A comparison was made to a baseline model with only spectrogram input. A geographical prior neural network was trained, separately, to estimate the probability of a species occurring at a given location. The output of this network was combined with the baseline CNN. As a second case study, temporal data and spectrograms were used as input to a multi-branch CNN for the detection of Hainan gibbon (Nomascus hainanus) calls, the world’s rarest primate. Our findings demonstrate that adding metadata to the bird song classifier significantly improves classification performance, with the highest improvement achieved using the geographical prior model (F1-score of 87.78% compared to 61.02% for the baseline model). The multi-branch CNNs also proved efficient (F1-scores of 76.87% and 78.77%) and simpler to use than the geographical prior. In the second case study, our findings revealed a decrease in false positives by 63% (94% of the calls were detected) when the metadata was used by the multi-branch CNN, and an increase of 19% in gibbon detection. This study has uncovered an exciting new avenue for improving classifier performance in bioacoustics. The methodology described in this study can assist ecologists, wildlife management teams, and researchers in reducing the amount of time spent analyzing large acoustic datasets obtained from passive acoustic monitoring studies. Our approach can be adapted and applied to other calling species, and thus tailored to other use cases.

Predictive Mouse Ultrasonic Vocalization Sequences: Uncovering Behavioral Significance, Auditory Cortex Neuronal Preferences, and Social-Experience-Driven Plasticity.

Mouse ultrasonic vocalizations (USVs) contain predictable sequential structures like bird songs and speech. Neural representation of USVs in the mouse primary auditory cortex (Au1) and its plasticity with experience has been largely studied with single-syllables or dyads, without using the predictability in USV sequences. Studies using playback of USV sequences have used randomly selected sequences from numerous possibilities. The current study uses mutual information to obtain context-specific natural sequences (NSeqs) of USV syllables capturing the observed predictability in male USVs in different contexts of social interaction with females. Behavioral and physiological significance of NSeqs over random sequences (RSeqs) lacking predictability were examined. Female mice, never having the social experience of being exposed to males, showed higher selectivity for NSeqs behaviorally and at cellular levels probed by expression of immediate early gene c-fos in Au1. The Au1 supragranular single units also showed higher selectivity to NSeqs over RSeqs. Social-experience-driven plasticity in encoding NSeqs and RSeqs in adult females was probed by examining neural selectivities to the same sequences before and after the above social experience. Single units showed enhanced selectivity for NSeqs over RSeqs after the social experience. Further, using two-photon Ca2+ imaging, we observed social experience-dependent changes in the selectivity of sequences of excitatory and somatostatin-positive inhibitory neurons but not parvalbumin-positive inhibitory neurons of Au1. Using optogenetics, somatostatin-positive neurons were identified as a possible mediator of the observed social-experience-driven plasticity. Our study uncovers the importance of predictive sequences and introduces mouse USVs as a promising model to study context-dependent speech like communications.SIGNIFICANCE STATEMENT Humans need to detect patterns in the sensory world. For instance, speech is meaningful sequences of acoustic tokens easily differentiated from random ordered tokens. The structure derives from the predictability of the tokens. Similarly, mouse vocalization sequences have predictability and undergo context-dependent modulation. Our work investigated whether mice differentiate such informative predictable sequences (NSeqs) of communicative significance from RSeqs at the behavioral, molecular, and neuronal levels. Following a social experience in which NSeqs occur as a crucial component, mouse auditory cortical neurons become more sensitive to differences between NSeqs and RSeqs, although preference for individual tokens is unchanged. Thus, speech-like communication and its dysfunction may be studied in circuit, cellular, and molecular levels in mice.

A lightweight multi-sensory field-based dual-feature fusion residual network for bird song recognition

Bird song recognition plays an important function in ecosystem balance monitoring, biodiversity detection, and biodiversity conservation. Due to the complexity of the natural environment, based on deep learning, there is a problem of information loss in extracting audio features with a single filter. Identifying bird sounds efficiently and quickly is still a challenge. To address this problem, a lightweight multi-sensory field dual-feature fusion residual network (LDFSRE-NET) is proposed in this paper. Firstly, using the feature extraction filters based on Mel and SincNet to extract birdsong’s low-frequency and timbre information. The proposed dual-feature fusion module (FFMS) is used to fuse the low-frequency and timbre information with the differences between the two feature sets. Secondly, the double-layer residual module (DBNet), connected by basicblock and downblock, is used as the backbone network for bird song recognition to improve the training speed. To improve the different perceptual fields of the backbone network, the 3 × 3 convolutional modules in the basicblocks of the two residual modules are replaced with a Diverse Branch Block. They make the network performs better on recognition tasks under complex situations of multiple branches. Then, the ShuffleAttention attention module is embedded between the two layers of the residual module for transferring its valid information, enhancing the spectrogram ripple feature, and further improving the network’s recognition performance. Finally, extensive experiments are conducted on three datasets: the self-built 30-class bird song dataset (Birdselfdata), the public datasets Birdsdata and Urbansound8K. The model proposed in this paper surpasses the state-of-the-art sound recognition model methods in terms of efficiency and accuracy. The recognition accuracy on these three datasets of this model are 96.75%, 96.46%, and 97.98%, with the F1-score of 96.79%, 96.39%, and 97.88%.

Song complexity is associated with habitat quality in an upland passerine

Understanding the influence of intrinsic (genetic and morphological) and extrinsic (geographical, environmental and social) factors on the performance and spatial differentiation of sexual signals, such as bird song, can help identify behavioural indicators of individual quality, habitat degradation and social environment. We used the Iberian Bluethroat Luscinia svecica azuricollis, a migratory bird that breeds in fragmented landscapes dominated by shrublands, as a case study to: (1) assess how a set of acoustic indicators of song performance are driven by intrinsic and extrinsic factors; and (2) contrast deterministic (adaptations to the environmental context and morphological constraints) vs. stochastic (differentiation by geographical isolation) explanations for song differentiation patterns. We explored acoustic indicators of song performance (spectral, temporal and song complexity) in relation to parameters related to genetic structure, body size, habitat type, habitat quality (assessed through a spatially explicit modelling approach) and social context (population abundance and songbird community composition). Then, we explored the contribution of genetic, geographical and environmental dissimilarity to song diversification across space. Our results highlight an association of song spectral variables with genetic structure and a significant connection between song complexity and duration with habitat quality. We found no relationship between social features and acoustic variables, or between song differentiation and genetic or geographical distances. There was, however, a correlation between song differentiation and environmental dissimilarity. We recommend the consideration of song complexity as an indicator of habitat quality.

Bio-inspired camouflage communication in air imitating birds songs

Radio communication is the most common means of communication nowadays, however, radio interference technology is becoming mature. Once radio communications are disrupted in the forest, people would be left in a passive position without any effective communication method. Sound is an also effective carrier of information. To realize effective communication in the forest environment, we proposed a bio-inspired camouflage communication strategy(BCCS) by imitating bird songs and took the Verditer Flycatcher as an example to demonstrate the feasibility of BCCS. We proposed a compound characteristics grouping coding method based on the tree structure to divide syllables into 32 feature groups. And five time–frequency(T-F) characteristics of syllables were extracted as grouping layers: syllable length, average frequency, frequency trend, the standard deviation of global frequency, and the difference of local frequency standard deviations. Then the correlation was used as the unique identification of a syllable within the group. Syllables in the feature group were screened to ensure high autocorrelation and low cross-correlation, which are the key basis in decoding. Each individual syllable can encode 8 bits of binary information through compound characteristics coding and correlation coding. Since the communication sequence is completely composed of the original bird songs and follows the regularities of distribution, BCCS has a good camouflage ability. Experiments were carried out to verify the feasibility and camouflage ability of BCCS in the forest environment.

Evolutionary and Ecological Processes Underlying Geographic Variation in Innate Bird Songs.

AbstractEcological and evolutionary processes underlying spatial variation in signals involved in mate recognition and reproductive isolation are crucial to understanding the causes of population divergence and speciation. Here, to test hypotheses concerning the causes of song divergence, we examine how songs of two sister species of Atlantic Forest suboscine birds with innate songs, the Pyriglena fire-eye antbirds, vary across their ranges. Specifically, we evaluated the influence of isolation by distance and introgressive hybridization, as well as morphological and environmental variation, on geographic variation in male songs. Analyses based on 496 male vocalizations from 63 locations across a 2,200-km latitudinal transect revealed clinal changes in the structure of songs and showed that introgressive hybridization increases both the variability and the homogenization of songs in the contact zone between the two species. We also found that isolation by distance, morphological constraints, the environment, and genetic introgression independently predicted song variation across geographic space. Our study shows the importance of an integrative approach that investigates the roles of distinct ecological and evolutionary processes that influence acoustic signal evolution.

A Study on the Soundscape Preferences of the Elderly in the Urban Forest Parks of Underdeveloped Cities in China

Against the backdrop of the global aging trend, the proportion of the elderly population is severely increasing in the urban areas of underdeveloped regions. Despite evidence that urban forest parks are effective at enhancing the physical and mental well-being of the elderly, little has been done to investigate the connection between urban forest parks and the elderly in underdeveloped regions, and landscape studies in particular are lacking. This study attempted to address this gap, using a subjective evaluation method in which 725 elderly respondents were engaged in a questionnaire survey on their soundscape preferences in the urban forest parks of an underdeveloped city in China. The results revealed the elderly people’s preferences for soundscapes, and a further analysis demonstrated the relationships between these preferences and landscape features. The effects of personal traits and living situations on soundscape preferences were determined by analyzing the impacts of living conditions, occupation, and education on soundscape preferences. By building a model with regression coefficients, the most powerful factors influencing soundscape choice were investigated. It was found that (1) the types of sound sources preferred by the elderly, in descending order, were natural sound, livestock sound, bird song, musical sound, other sounds. (2) The differences among education, occupation, and age all affected the participants’ soundscape preferences, i.e., the mean values of the soundscape preferences among older adults varied with education, occupation, and age. The mean value of soundscape preference was higher among older adults who had received higher education, were government officials and business managers, and belonged to higher age groups. (3) Among the various factors influencing the soundscape preference of the elderly, the most influential factors were the length of time spent in the waterfront environment, the time spent in the forest park, and the importance of road signs. (4) The preference for soundscapes was strongly connected with happiness in life. (5) Wearing a mask significantly reduced soundscape perception scores under epidemic conditions, while vaccinated individuals were more tolerant of various noises. Recommendations for landscape design to improve the soundscape perception of elderly people are accordingly provided.

Pykanto: A python library to accelerate research on wild bird song

Abstract Studying the vocalisations of wild animals can be a challenge due to the limitations of traditional computational methods, which often are time‐consuming and lack reproducibility. Here, I present pykanto, a new software package that provides a set of tools to build, manage, and explore large sound databases. It can automatically find discrete units in animal vocalisations, perform semi‐supervised labelling of individual repertoires with a new interactive web app and feed data to deep learning models. pykanto can be used to streamline research on, for example, individual vocal signatures and acoustic similarity between individuals and populations. To demonstrate its capabilities, I put the library to the test on the vocalisations of male great tits in Wytham Woods, near Oxford, UK. The results show that the identities of individual birds can be accurately determined from their songs and that the use of pykanto improves the efficiency and reproducibility of the process.

Evaluating Data Quality and Changes in Species Identification in a Citizen Science Bird Monitoring Project

Citizen science has potential to provide multiple benefits to participants and the professional scientific community, and those benefits can be realized if citizen science projects are intentionally designed to achieve research objectives, and if participants have the skills, knowledge, and training to collect high-quality data. Using three years of data from a citizen science bird monitoring project in Salt Lake City, Utah, we assessed bird songs and calls learned by volunteers, and compared species detections, number of birds, and distance measurements between point counts by citizen scientists and professional biologists. We found significant increases in correct species identification for citizen scientists after going through the training program; the average percentage of bird songs and calls identified rose from 42.5% before training to 72.7% after training (p < 0.00001). For two data quality metrics, citizen scientists and professional biologists collected similar quality data: the average number of birds and average detection distances were not significantly different for point counts conducted by citizen scientists and professional biologists in the same locations. However, professional biologists identified an average of 1.48 more species than citizen scientists (p < 0.00001). Our findings emphasize the importance of evaluating training programs and data accuracy for citizen science projects. In instances in which citizen scientists may not be performing at the same level as professional biologists, identifying these patterns ensures that they can be fully explained and accounted for during data analysis.

The relationships of breeding stage to daytime singing behaviour and song perch height in Bermuda white‐eyed vireos Vireo griseus bermudianus

Bird song is crucial for attracting mates and defending territories, but different types of song or different singing behaviours may be involved in acquiring or maintaining each resource. Furthermore, male songbirds may adjust when and where they sing throughout the breeding season, depending on their breeding stage. However, such relationships remain untested in several avian taxa. Here, we studied male Bermuda white‐eyed vireos Vireo griseus bermudianus, a passerine with two distinct song types (discrete and rambling), to test the mate attraction and territory defence hypotheses. We compare song production and song perch height among different stages of the breeding season and during the non‐breeding season. We show that male vireos produce both song types during the breeding and non‐breeding seasons, suggesting dual roles in mate choice and territorial defence. Song production did not differ significantly between the breeding and non‐breeding seasons, but, within the breeding season, males without nesting duties sang significantly more songs than males with nesting duties. Song perch height was higher during the breeding season versus non‐breeding season, among males without nesting duties compared to males with nesting duties, and when males produced discrete versus rambling songs. Our findings suggest that male vireos increase their conspicuousness to prospecting females by increasing song production and song perch height, and that they sing during the breeding and non‐breeding seasons to defend year‐round territories. Collectively, our study supports the mate attraction and territory defence hypotheses of bird song.

Comparative genomics of two Empidonax flycatchers reveal candidate genes for bird song production.

Whole-genome level comparisons of sister taxa that vary in phenotype against a background of high genomic similarity can be used to identify the genomic regions that might underlie their phenotypic differences. In wild birds, this exploratory approach has detected markers associated with plumage coloration, beak and wing morphology, and complex behavioral traits like migration. Here, we use genomic comparisons of two closely related suboscine flycatchers (Empidonax difficilis and E. occidentalis) and their hybrids to search for candidate genes underlying their variation in innate vocal signals. We sequenced the genomes of 20 flycatchers that sang one of two species-specific parental song types and 14 putative hybrid individuals with intermediate song types. In the resulting genomic comparisons, we found six areas of high differentiation that may be associated with variation in non-learned songs. These narrow regions of genomic differentiation contain a total of 67 described genes, three of which have been previously associated with forms of language impairment and dyslexia in humans, and 18 of which are known to be differentially expressed in the song nuclei regions of the avian brain compared to adjacent parts of the avian brain. This 'natural experiment' therefore may help identify loci associated with song differences that merit further study across bird lineages with both learned and innate vocalizations.

Hearing Durational Process in A Huli Song

Hearing Durational Process in A Huli Song

Putting on your “woods ears”: A lesson in listening

AbstractA simple exercise of active listening to bird vocalizations in a natural setting was evaluated in an introductory class (FW101L) at Michigan State University. This class is geared toward first‐ or second‐year students and is intended to broaden their skills and knowledge as naturalists. This activity is shown to have a lasting impact on students evaluated in 2015 and 2016 via feedback in a reflective essay students submit covering the entire course. I posit that focusing student attention on bird songs and calls increases the saliency of these sounds, and a positive feedback loop is set up whereby these sounds are more readily detected in future outdoor experiences, which is the intended outcome. This exercise is an effective component of an outdoor experiential class, but more broadly emphasizes the role that saliency has on student learning.

Yellow Cardinal (Gubernatrix cristata) males respond more strongly to local than to foreign dialects

Bird song is a culturally transmitted behavioural trait strongly tied to territory defence and mate attraction in oscine passerines. Divergent song variations can appear in different populations of the same species as a consequence of multiple factors generating geographically distinct songs also termed dialects. Most studies show that males react more strongly towards local dialects, potentially as a by‐product of evolutionary divergence between populations, although other hypotheses have been suggested. In addition, females are hypothesized to show stronger responses to local songs as this may lead to greater reproductive success. In this context, song may become a prezygotic barrier for reproduction. The Yellow CardinalGubernatrix cristatais an endangered species of the South American thorny shrubland that has suffered from population fragmentation due to habitat destruction and its capture for the illegal wildlife trade. Genetically different groups that sing their own dialect have been studied in the context of a conservation programme that intends to restore and preserve natural populations by releasing individuals that are recovered from the illegal pet market back into their area of origin. Although dialects have been identified, it remains unknown whether there is vocal recognition between the populations. In this study, we performed playback experiments in two populations and found that male Yellow Cardinals reacted more strongly to the local dialect, although the foreign dialect was still recognized. This information is important as it is not always possible to genetically assign an individual to its geographical origin before release, which could contribute to a secondary contact scenario where dialects play a critical role for settlement and reproduction.

ArcticBirdSounds: An open-access, multiyear, and detailed annotated dataset of bird songs and calls.

Tracking biodiversity shifts is central to understanding past, present, and future global changes. Recent advances in bioacoustics and the low cost of high-quality automatic recorders are revolutionizing studies in biogeography and community and behavioral ecology with a robust assessment of phenology, species occurrence, and individual activity. This large volume of acoustic recordings has recently generated a plethora of datasets that can now be handled automatically, mostly via big data methods such as deep learning. These approaches need high-quality annotations to classify and detect recorded sounds efficiently. However, very few strongly annotated datasets-that is, with detailed information on start and end time of each vocalization-are openly accessible to the public. Moreover, these datasets mostly cover temperate species and are usually limited to a single year of recordings. Here, we present ArcticBirdSounds, the first open-access, multisite, and multiyear strongly annotated dataset of arctic bird vocalizations. ArcticBirdSounds offers 20 h of annotated recordings over 2 years (2018, 2019), taken from 15 distinct plots within six locations across the Arctic, from Alaska to Greenland. Recordings cover the arctic vertebrates' breeding period and are evenly spaced during the day; they capture most species breeding there with 12,933 temporal annotations in 49 classes of sounds. While these data can be used for many pressing ecological questions, it is also a unique resource for methodological development to help meet the challenges of fast ecosystem transformations such as those happening in the Arctic. All data, including audio files, annotation files, and companion spreadsheets, are available in an Open Science Framework repository published under a CC BY 4.0 License.

Emergent Sonification: Using Computational Media to Communicate the Anthropocene in ByrdBot

This research note describes ByrdBot, a science communication tool that leverages bird songs to communicate data regarding human impacts on the environment. With ByrdBot, listeners can compare simulated soundscapes of 1970, 2017, and 2065 to immediately, and viscerally, experience decades of past or projected future environmental change. The communication tactic of ByrdBot—what we call emergent sonification—is discussed as one that capitalizes on computational media to facilitate attunement to nonhuman voices and, subsequently, to offer an affective grasping of the impacts of such phenomena as habitat destruction and climate change on wildlife displacement and loss.