Birdsong Research Articles

SDFIE-NET – A self-learning dual-feature fusion information capture expression method for birdsong recognition

Bird recognition is important for the monitoring of bird populations and the protection of ecosystems. Identifying birds through image forms can be difficult due to the complexity of natural environments. Song-based bird recognition allows for bird identification with only a small amount of background noise introduced, however, efficiently recognizing bird songs remains a challenging task. Based on this problem, this paper proposed a self-learning dual-feature fusion information capture expression method (SDFIE-NET) for recognizing birdsong. Firstly, using the Mel filter excerpt the low-frequency characteristics of the bird song. Since fixed-parameter filters are incapable of achieving different feature extraction effects based on different birdsong. In this paper, we incorporate a fully learnable audio classification front-end Leaf architecture for the extraction of bird song feature information, which can self-learn different extraction parameters for the birdsong. Effectively combining the high-frequency feature information and low-frequency differences acquired by the two approaches corresponds to the declared dual-feature fusion module (SCDFF), reducing information redundancy and improving characterization capability. Secondly, the backbone network utilizes SDFIE-NET, which is composed of the Fused-MBConv module and modified CA-MBConv module. The Criss-Cross Attention module is added after each layer composed of Fused-MBConv modules. This improves the speed and accuracy of effective information transfer between internal modules and increases the expressive power of the model at the pixel level. To enhance the anti-interference and generalization ability of the model, we constructed a self-made dataset (Bird_alldata) consisting of 30 kinds of birdsong. On this dataset, we performed a variety of experiments, and recognition accuracy reached 95.77 % and the F1-score reached 95.52 %. Generalization experiments were conducted on the environmental sound dataset Urbansound8K and the bird song dataset Birdsdata, and the model achieves recognition accuracies of 94.05 % and 94.10 % on the two datasets, with F1-scores of 94.21 % and 94.05 %, respectively.

Analysis Of Synthesizing Natural Sounds Based on Nyquist

As a matter of fact, many captivating sounds emanate from the natural world, yet their inherent dynamism presents a difficult challenge for replication by existence instruments in recent years. For this reason, computers rarely manage to faithfully replicate these natural sounds. With this in mind, this paper revolves around capturing these beautiful sounds within algorithms, which are later integrated into a demonstrative meditative composition. To be specific, this study will detail demonstrate the methods as well as techniques employed to recreate sounds such as cricket chirps, bird songs, and rain. In detail, the program Nyquist will be introduced in brief as well as applied to achieve the goal. Additionally, a concise overview of the compositional process will be provided and analyzed at the same time. According to the analysis, the current drawbacks for the approaches will be analyzed. In addition, the future prospects will be discussed accordingly. Overall, these results shed light on guiding further exploration of natural sounds synthesizing.

Associations between bird song attributes and noise, light, date, and temperature: Vermilion flycatchers (Pyrocephalus rubinus) sing shorter and higher pitched songs in territories with more artificial light at night

Urban noise and artificial light at night (ALAN) are global pollutants affecting animal acoustic communication. A previous study that measured noise, but not ALAN, found that vermilion flycatchers produce longer songs in noisier territories. A subsequent study in another population, that measured noise and ALAN, found that males emit longer songs in brighter, but not noisier, territories. The negative result could be due a lack of sufficient noise variation in the latter study. We evaluated the contribution of noise and ALAN on song length, provided sufficient variation in noise and ALAN. We also measured song peak frequency. During the dawn chorus, we recorded songs of 41 vermilion flycatchers in two populations (UNAM and PERM), and measured noise, ALAN, and temperature. For the population with sufficient noise and ALAN variation (UNAM), we confirmed that song length was positively associated with noise; it was also negatively associated with ALAN, especially in those males recorded late in the breeding season. At UNAM, we found an overall positive association between song pitch and ALAN; additionally, males singing at relatively low ambient temperatures (12-15 °C) produced higher pitched songs as ALAN increased. Those singing closer to new moon and at relatively low ambient noise levels (43-54 dB[A]) sang higher pitched songs in brighter territories; these two results should be taken cautiously. Our results show that light, noise, date, and temperature are associated in unexpected ways with song length and pitch, potentially affecting song information content and sexual selection.

Male song structure predicts offspring recruitment to the breeding population in a migratory bird.

Bird song is a classic example of a sexually selected trait, but much of the work relating individual song components to fitness has not accounted for song typically being composed of multiple, often-correlated components, necessitating a multivariate approach. We explored the role of sexual selection in shaping the complex male song of house wrens (Troglodytes aedon) by simultaneously relating its multiple components to fitness using multivariate selection analysis, which is widely used in insect and anuran studies but not in birds. The analysis revealed significant variation in the form and strength of selection acting on song across different selection episodes, from nest-site defense to recruitment of offspring to the breeding population. Males that sang more song typically employed in close communication sired more offspring that were subsequently recruited to the breeding population than those that sang more far-communication song. However, this relationship was not consistent across earlier selection episodes, as evidenced by non-linear selection acting on these song components in other contexts. Collectively, our results present a complex picture of multivariate selection on male song structure that would not be evident using univariate approaches and suggest possible trade-offs within and among song components at different points of the breeding season.

Intraspecific variation of three plumage-cryptic bulbul species

Bird songs and morphometry are frequently used to distinguish bird species which are plumage-cryptic, or to delineate boundaries of subspecies. Rubigula flaviventris, Pycnonotus finlaysoni and Brachypodius melanocephalos have each recognized subspecies along the Thai-Malay Peninsula, however the range-limit of the subspecies distribution is still ambiguous and genetic data from a previous own study suggests a revision of the subspecies distribution and the subspecies barriers is warranted. To analyze the subspecies boundaries based on potential song and morphometric traits, we recorded, measured, and analyzed song characters and morphometry of the three bulbuls along the Thai-Malay Peninsula. The geographic variation of songs and morphometry differs significantly for specific traits; however, we could not find any trait discontinuities that would be in accordance with previously documented phylogeographic divides. Instead trait variation was mostly clinal and followed a North-South gradient in line with Bergmann’s rule. Due to the lack of distinctiveness of any subspecific taxon, we conclude that shallow genetic divides on the Thai-Malay Peninsula are not indicative of potential species-level splits in the three species of our study.

Human presence is positively related to the number of bird calls and songs: Assessment in a national park

Human disturbance has been shown to provoke physiological and behavioral responses in birds, so nature-based tourism might reduce bird abundance and diversity. The negative consequences of human disturbance might be expected to be maximized during eventual massive events in highly protected areas such as national parks. In this study, the consequences for soundscapes of human presence and disturbance of thousands of visitors during an ornithological fair (massive event) on the bird community of the Monfragüe National Park (Spain) were analyzed. We found that the number and diversity of bird vocalizations did not decrease during the massive event. In contrast, the presence of people in the Monfragüe National Park was associated with an increase in the number and diversity of vocalizations. The effect of human presence on the number of calls and songs differed: the number of calls mainly increased during the massive event when people were present, while the number of songs increased when people were present, particularly during the measurement campaign without the massive event. The human shield hypothesis, along with other behavioral and environmental factors, might potentially explain the results obtained.

A RYÛSUKE HAMAGUCHI MOVIE: ‘WALDEN'

<p>Ryûsuke Hamaguchi (1978) is one of the most important directors of contemporary cinema. He has won many awards with his films at the world's major film festivals such as Berlin, Cannes, and Venice and he received the Academy Award for Best International Feature Film in 2022 with his movie <em>‘Drive My Car’</em>. Hamaguchi’s <em>‘Walden’</em> (2022) is one of Hamaguchi's recent movies. <em>‘Walden’</em> is a co-production of Austria, and Japan, premiered at The Festival Viennale (2022). Within Hamaguchi’s relatively mainstream cinema poetics, the movie <em>‘Walden’</em> is an experimental work that invites the spectator to a deep contemplation. The film is a 2-minute, almost monochrome record of a water surface with reflections of trees, raindrops, vibration waves, water rings, bird songs, swarming insects, several sounds of nature, a floating leaf, etc. In addition to the sounds of nature, the film includes a female voice excerpt with music from Douglas Sirk's <em>‘All That Heaven Allows’</em> (1955). Jean Wyman who is the protagonist “Cary Scott” of <em>‘All That Heaven Allows’</em>, reads some lines from Henry David Thoreau's book Walden (1854). This movie at the same time is a documentary record of a water surface; a free cinematographic adaptation of Henri David Thoreau's autobiographical book <em>‘Walden’</em>, a deep romantic landscape through continuous and random visual variations occurring on the surface and a crystallization of Hamaguchi’s style. Hamaguchi’s <em>‘Walden’</em> surface also becomes an interface where Hamaguchi the artist, and the spectator meet temporally to explore the noema of existence, nature, and cinema within a Heideggerian-verse. Besides the 2- minute durée of the film and its surface subject indicates a continuous oscillation between Kronos and Aion in the Deleuzian sense.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0758/a.php" alt="Hit counter" /></p>

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.

Social Evaluation of Female Preferences in Birds’ Songs and Songs Function in Mating

The selection of a mate is influenced by bird songs, which are known for their complexity and diversity. This essay delves into the realm of female mate preferences within various avian species, presenting both supportive and contradictory evidence regarding the importance of larger song repertoires. It emphasizes the distinction between controlled laboratory settings and ecologically authentic field studies, revealing nuanced findings. Furthermore, it explores additional factors like individual variation, song complexity, cognitive abilities, and contextual adaptability to elucidate this mixed outcome, enriching our comprehension of avian communication dynamics. Male bird songs emerge as essential conduits for conveying vital information that enables females to make informed choices about potential mates. Through song types and tones, they transmit demographic information, social status, and physical condition, offering females a means of remote evaluation. The examination of distinct song behaviors, including duets, flight songs, and the dawn chorus, highlights their significance as indicators of partnership, strength, and commitment. The varied strategies employed by male birds and the selective decisions made by females underscore the profound role of avian songs in shaping relationships and ensuring the perpetuation of species. This marks a substantial contribution to our understanding of avian mate selection dynamics.

Nighthawk: Acoustic monitoring of nocturnal bird migration in the Americas

Abstract Animal migration is one of nature's most spectacular phenomena, but migratory animals and their journeys are imperilled across the globe. Migratory birds are among the most well‐studied animals on Earth, yet relatively little is known about in‐flight behaviour during nocturnal migration. Because many migrating bird species vocalize during flight, passive acoustic monitoring shows great promise for facilitating widespread monitoring of bird migration. Here, we present Nighthawk, a deep learning model designed to detect and identify the vocalizations of nocturnally migrating birds. We trained Nighthawk on the in‐flight vocalizations of migratory birds using a diverse dataset of recordings from across the Americas. Our results demonstrate that Nighthawk performs well as a nocturnal flight call detector and classifier for dozens of avian taxa, both at the species level and for broader taxonomic groups (e.g. orders and families). It achieves an average precision score above 0.80 for 50 species and a mean average precision of 0.96 across 4 orders. The model accurately quantified nightly nocturnal migration intensity (80% variation explained) and species phenology (78% variation explained) and performed well on data from across North America. Incorporating modest amounts of additional annotated audio (50–120 h) into model training yielded high performance on target datasets from both North and South America (average precision on order Passeriformes >0.99). By monitoring the vocalizations of actively migrating birds, Nighthawk provides a detailed window onto nocturnal bird migration that is not presently attainable by other means (e.g. radar or citizen science). Scientists, managers and practitioners could use acoustic monitoring with Nighthawk for a number of applications, including: monitoring migration passage at wind farms; studying airspace usage during migratory flights; monitoring the changing migrations of species susceptible to climate change; and revealing previously unknown migration routes and behaviours. Overall, this work will empower diverse stakeholders to efficiently monitor migrating birds across the Western Hemisphere and collect data in aid of science and conservation.

The effects of anthropogenic noise and urban habitats on song structure in a vocal mimic; the gray catbird (Dumetella carolinensis) sings higher frequencies in noisier habitats

In urban and human-modified landscapes, animals face novel selection pressures resulting from differences in habitat structure and increased anthropogenic noise. Urban noise pollution can negatively impact songbirds because low-frequency noise often masks portions of birds’ mating signals and reduces signal transmission. Previous research has demonstrated that the songs of birds in more urban habitats have structural differences that can enhance signal transmission when noise is present. The majority of these studies have focused on species that deliver short, stereotyped songs and have limited repertoires. Gray catbirds (Dumetella carolinensis, family: Mimidae) sing long bouts containing imitated, improvised, and invented song elements, and therefore may have an increased ability to vary songs in response to noise. We hypothesized that aspects of developed habitats including loud anthropogenic noise and changes to land cover would impact catbirds’ song structural parameters, including song minimum, peak, and maximum frequency, frequency bandwidth, and entropy. We recorded and processed songs from 42 male catbirds and analyzed over 18,000 song elements from sites along an urban gradient from western Virginia to the Washington, DC metropolitan region. We quantified the urban intensity at each site-centroid based on percent canopy cover, percent impervious surface, and noise level. Song features such as minimum, maximum, and peak frequency increased significantly as noise levels increased, demonstrating that catbirds in noisier areas sing higher frequency songs compared to individuals in quieter habitats. Land cover variables also significantly predicted certain song features such as maximum frequency (impervious surface) or entropy (canopy cover). These structural differences in catbird song can limit the negative effects of environmental noise-masking, even for their long song bouts, and suggest that vocal mimics respond to anthropogenic noise. Future studies could investigate repertoire size and composition along an urban gradient and if these structural differences lead to functional consequences for the songs of vocal mimics.

Machine learning reveals that climate, geography, and cultural drift all predict bird song variation in coastal Zonotrichia leucophrys

Abstract Previous work has demonstrated that there is extensive variation in the songs of White-crowned Sparrow (Zonotrichia leucophrys) throughout the species range, including between neighboring (and genetically distinct) subspecies Z. l. nuttalli and Z. l. pugetensis. Using a machine learning approach to bioacoustic analysis, we demonstrate that variation in song is correlated with year of recording (representing cultural drift), geographic distance, and climatic differences, but the response is subspecies- and season-specific. Automated machine learning methods of bird song annotation can process large datasets more efficiently, allowing us to examine 1,913 recordings across ~60 years. We utilize a recently published artificial neural network to automatically annotate White-crowned Sparrow vocalizations. By analyzing differences in syllable usage and composition, we recapitulate the known pattern where Z. l. nuttalli and Z. l. pugetensis have significantly different songs. Our results are consistent with the interpretation that these differences are caused by the changes in characteristics of syllables in the White-crowned Sparrow repertoire. This supports the hypothesis that the evolution of vocalization behavior is affected by the environment, in addition to population structure.

Well-being and multisensory urban parks at different ages: The role of interoception and audiovisual perception

The aim of the present work was to verify whether the perception of visual and acoustic characteristics of urban parks is influenced by the age of the participants. Fifty young (average age 25 years) and fifty elderly (average age 66 years) people were shown with combinations of different types of urban parks (i.e., green, colorful parks and squares) and different types of sound (i.e., bird song, water, chattering and traffic). For each audio-video scenario, participants indicated how calm, happy and stimulated it made them feel and how pleasant, exciting and attractive each scenario was. In addition, participants performed a cross-modal attention task and filled out an interoceptive awareness questionnaire. The results showed that both young and elderly participants preferred green parks with natural sounds. In addition, elderly participants positively evaluated colorful parks over squares. Interestingly, young participants gave more importance to the evaluation of visual-only elements than auditory-only elements, whereas elderly participants gave more importance to the acoustic characteristics of scenarios than the visual ones. Finally, in the elderly group, both attentional abilities and interoceptive awareness contributed to the positive evaluation of urban parks. Theoretically, the results suggest that both signals from the body and from the external world contribute to a holistic representation of the environment. Cross-modal attention may play a crucial role in this process. On an applied level, the data suggest that the design of colorful parks and soundscapes that evoke nature should be encouraged, especially for older populations.

Software-dependent biases in the recognition of di- and tri-syllabic bird songs can create false interpretations of bird abundance and singing activity

The use of autonomous recording units (ARUs) for passive acoustic monitoring has recently gained a lot of importance. It is now widely used for scientific purposes and has been increasingly applied in the field of conservation biology. However, while ARUs may greatly increase cost-effectiveness in monitoring, they are not exempt from possible biases. In this study, we compared the performances of three different software in recognizing bi- and tri-syllabic bird songs and tested if their results varied significantly in a real setting. We focused on the interpretation of the relative abundance among habitats and the time of vocal activity, also using different numbers of training records to assess if the results of the software improved. Even with the quality measures of the recognizer being consistent with those of previously published studies, in several cases the results produced significantly different interpretations depending on the software used. While increasing the number of training records slightly improved the overlap in the estimates of activity among the different software, differences in the relative abundance of each song were present even with a fairly solid number of training records. Our results suggest that reliable biological interpretations might only be attained with a large number of training records and very high values of precision, recall, and the F-score. In fact, even with very high precision and F-scores, lower values for recall led not only to statistically significant differences, but also to different interpretations of relative abundance and time of activity between the two best-performing software. Thus, great caution must be taken in interpreting the results of bioacoustics studies that use automated recognition, and standards of minimum quality should be created for recognizers in scientific and technical studies.

Multilevel Bayesian analysis of monk parakeet contact calls shows dialects between European cities.

Geographic differences in vocalizations provide strong evidence for animal culture, with patterns likely arising from generations of social learning and transmission. Most studies on the evolution of avian vocal variation have predominantly focused on fixed repertoire, territorial song in passerine birds. The study of vocal communication in open-ended learners and in contexts where vocalizations serve other functions is therefore necessary for a more comprehensive understanding of vocal dialect evolution. Parrots are open-ended vocal production learners that use vocalizations for social contact and coordination. Geographic variation in parrot vocalizations typically take the form of either distinct regional variations known as dialects or graded variation based on geographic distance known as clinal variation. In this study, we recorded monk parakeets (Myiopsitta monachus) across multiple spatial scales (i.e., parks and cities) in their European invasive range. We then compared calls using a multilevel Bayesian model and sensitivity analysis, with this novel approach allowing us to explicitly compare vocalizations at multiple spatial scales. We found support for founder effects and/or cultural drift at the city level, consistent with passive cultural processes leading to large-scale dialect differences. We did not find a strong signal for dialect or clinal differences between parks within cities, suggesting that birds did not actively converge on a group level signal, as expected under the group membership hypothesis. We demonstrate the robustness of our findings and offer an explanation that unifies the results of prior monk parakeet vocalization studies.

Identification of dialects and individuals of globally threatened yellow cardinals using neural networks

Audio-based analysis of bird songs has proven to be a valuable practice for the growth of knowledge in the fields of ethology and ecology. In recent years, machine learning techniques applied to audio field recordings of bird calls have yielded successful results in studying population distributions and identification of individuals for their monitoring in a variety of bird species. This offers promising possibilities in the study of social behavior, biodiversity, and conservation strategies for birds. In this work, we trained deep learning models, directly from the sonograms of audio field recordings, to investigate the statistical properties of vocalizations in an endangered bird species, the Yellow Cardinal, Gubernatrix cristata. This research marks the first successful application of this method to an endangered species. Our results indicate the presence of vocal signatures that reflect similarities in songs of individuals that inhabit the same region, determining dialects, but which also show differences between individuals. These differences can be exploited by a deep learning classifier to discriminate the bird identities through their songs. Models trained with data labeled by regions showed a good performance in the recognition of dialects with a mean accuracy of 0.84 ± 0.04, significantly higher than the accuracy obtained by chance. Precision and recall values also reflected the classifier's ability to find alike vocal patterns in the songs of neighboring individuals. Models trained with data labeled by individuals showed an accuracy of 0.63 ± 0.03, significantly higher than that obtained by chance. However, the individual discrimination model showed greater confusion with neighboring individuals. This reflects a hierarchical structure in the characteristics of the Yellow Cardinal's vocalization, where the intra-individual variability is lower than the inter-individual variability, but it is even lower than the variability obtained when individuals inhabit different regions, providing evidence of the existence of dialects. This reinforces the results of previous works but also offers an automated method for characterizing cultural units within the species. Along with genetic data, this method could help better define management units, thereby benefiting the success of reintroduction of individuals of Yellow Cardinal recovered from the illegal trade. Moreover, the novelty of individual discrimination using neural networks for the Yellow Cardinal, which has limited data availability, shows promise for non-invasive acoustic monitoring strategies with potentially relevant implications for its conservation.

Technological advances for getting insight into the learning capacities of birds in the vocal domain

Abstract Birds produce different types of sounds in different contexts such as begging for food in youngsters, alerting to a danger, defending a territory or attracting a sexual partner. About half of the bird species are able to transform their vocalizations through imitation, improvisation or invention of sounds. Here we review the different experimental procedures that have been used to study the learning capacities of birds in the vocal domain and in the auditory domain, with a particular emphasis on recent technological developments. Nowadays, it is possible to record individual vocalizations of birds living in social groups or to record continuously the vocal ontogeny of birdsong. In conditioning experiments, new paradigms have successfully replaced food rewards with a socio-sexual reward. It is possible to engage in vocal interactions with a bird using dedicated computer systems. In both the laboratory and more recently in the field, different techniques have been used to train young oscine songbirds to learn from acoustic models. The use of virtual social environments and robots as social agents are also promising avenues. All together, these new techniques will permit researchers to explore more deeply the umwelt of bird species.

Physiological constraints and cognitive chunking: Sequence organization in the songs of zebra finches (Taeniopygia guttata).

Learned bird songs often have a hierarchical organization. In the case of zebra finches, each bird's song is made up of a string of notes delivered in a stereotyped sequence to form a "motif," and motifs are repeated to form a song bout. During song learning, young males copy "chunks" of two or more consecutive notes from their tutors' songs. These chunks are represented as distinct units within memory (during learning) and within motor systems (during song production). During song performance, motifs may deviate from the learned sequence by stopping short, starting late, or by skipping, inserting, or repeating notes. We measured acoustic and temporal variables related to the respiratory and vocal physiology of song production and asked how they related to deviations from each bird's "canonical" sequence. The best predictor of deviations from that sequence was the duration of the silent interval between notes, when inspiration normally occurs. Deviations from the canonical motif occurred less often after higher-pitched notes, perhaps because a high-low sequence forms a prosodic unit. Premature stops often followed louder and longer notes, suggesting that respiratory and muscular physiology influence the location of such stops. Boundaries between the learned chunks of a male's motif predicted where and how often noncanonical starts occurred. Physiological and cognitive elements also interacted to define the segmentation of zebra finch song sequences. Long silent intervals between notes were associated both with physiology (inspirations) and with the cognitive boundaries of learned chunks-and hence with deviations from the canonical motif. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.