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Birdsong is among the most beautiful and complex sounds produced in the natural world. Modern ornithological research into the vocal communication of birds continues to open up new fields of research in neurobiology, ethology and evolutionary biology. Birdsong also fascinates musicographers and musicologists interested in, on the one hand, the presence of birds in music, and, on the other — theoretical questions concerning the ontology, aesthetic qualities and semiotics of birdsong resembling music in some respects. Reflection on this direct, easily accessible and natural sound experience of human beings is the subject of the present article. It is an attempt to answer the question about the source of expression and aesthetic meaning of bird-song for humans, about how bird “music” resonates with the human world. In particular — about whether the expressive meaning of birdsong is embedded and contained in it or whether its source is the listener’s subjective projection.

Some animal vocalizations develop reliably in the absence of relevant experience, but an intriguing subset of animal vocalizations is learned: they require acoustic models during ontogeny in order to develop, and the learner's vocal output reflects those models. To what extent do such learned vocalizations reflect phylogeny? We compared the degree towhich phylogenetic signal is present in vocal signals fromawide taxonomic range of birds, including both vocal learners (songbirds) and vocal non-learners. We used publically available molecular phylogenies and developed methods to analyse spectral and temporal features in a carefully curated collection of high-quality recordings of bird songs and bird calls, to yield acoustic distance measures. Our methods were initially developed using pairs of closely related North American and European bird species, and then applied to a non-overlapping randomstratified sample of European birds. We found strong similarity in acoustic and genetic distances, which manifested itself as a significant phylogenetic signal, in both samples. In songbirds, both learned song and (mostly) unlearned calls allowed reconstruction of phylogenetic trees nearly isomorphic to the phylogenetic trees derived from genetic analysis. We conclude that phylogeny and inheritance constrain vocal structure to a surprising degree, even in learned birdsong. This article is part of the theme issue 'Vocal learning in animals and humans'.

Social processes profoundly influence speech and language acquisition. Despite the importance of social influences, little is known about how social interactions modulate vocal learning. Like humans, songbirds learn their vocalizations during development, and they provide an excellent opportunity to reveal mechanisms of social influences on vocal learning. Using yoked experimental designs, we demonstrate that social interactions with adult tutors for as little as 1 d significantly enhanced vocal learning. Social influences on attention to song seemed central to the social enhancement of learning because socially tutored birds were more attentive to the tutor’s songs than passively tutored birds, and because variation in attentiveness and in the social modulation of attention significantly predicted variation in vocal learning. Attention to song was influenced by both the nature and amount of tutor song: Pupils paid more attention to songs that tutors directed at them and to tutors that produced fewer songs. Tutors altered their song structure when directing songs at pupils in a manner that resembled how humans alter their vocalizations when speaking to infants, that was distinct from how tutors changed their songs when singing to females, and that could influence attention and learning. Furthermore, social interactions that rapidly enhanced learning increased the activity of noradrenergic and dopaminergic midbrain neurons. These data highlight striking parallels between humans and songbirds in the social modulation of vocal learning and suggest that social influences on attention and midbrain circuitry could represent shared mechanisms underlying the social modulation of vocal learning.

This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.

Bioacoustic monitoring is an effective and minimally invasive method to study wildlife ecology. However, even the state-of-the-art techniques for analyzing birdsongs decrease in accuracy in the presence of extraneous signals such as anthropogenic noise and vocalizations of non-target species. Deep supervised source separation (DSSS) algorithms have been shown to effectively separate mixtures of animal vocalizations. However, in practice, recording sites also have site-specific variations and unique background audio that need to be removed, warranting the need for site-specific data. Here, we test the potential of training DSSS models on site-specific bird vocalizations and background audio. We used a semiautomated workflow using deep supervised classification and statistical cleaning to label and generate a site-specific source separation dataset by mixing birdsongs and background audio segments. Then, we trained a deep supervised source separation (DSSS) model with this generated dataset. Because most data is passively-recorded and consequently noisy, the true isolated birdsongs are unavailable which makes evaluation challenging. Therefore, in addition to using traditional source separation (SS) metrics, we also show the effectiveness of our site-specific approach using metrics commonly used in ornithological analyses such as automated feature labeling and species-specific trilateration accuracy. Our approach of training on site-specific data boosts the source-to-distortion, source-to-interference, and source-to-artifact ratios (SDR, SIR, and SAR) by 9.33 dB, 24.07 dB, and 3.60 dB respectively. We also find our approach allows for automated feature labeling with single-digit mean absolute percent error and birdsong trilateration accuracy with a mean simulated trilateration error of 2.58 m. Overall, we show that site-specific DSSS is a promising upstream solution for wildlife audio analysis tools that break down in the presence of background noise. By training on site-specific data, our method is robust to unique, site-specific interference that caused previous methods to fail.

Closely related species often exhibit similarities in appearance and behaviour, yet when related species exist in sympatry, signals may diverge to enhance species recognition. Prior comparative studies provided mixed support for this hypothesis, but the relationship between sympatry and signal divergence is likely nonlinear. Constraints on signal diversity may limit signal divergence, especially when large numbers of species are sympatric. We tested the effect of sympatric overlap on plumage colour and song divergence in wood-warblers (Parulidae), a speciose group with diverse visual and vocal signals. We also tested how number of sympatric species influences signal divergence. Allopatric species pairs had overall greater plumage and song divergence compared to sympatric species pairs. However, among sympatric species pairs, plumage divergence positively related to the degree of sympatric overlap in males and females, while male song bandwidth and syllable rate divergence negatively related to sympatric overlap. In addition, as the number of species in sympatry increased, average signal divergence among sympatric species decreased, which is likely due to constraints on warbler perceptual space and signal diversity. Our findings reveal that sympatry influences signal evolution in warblers, though not always as predicted, and that number of sympatric species can limit sympatry's influence on signal evolution.

Most studies of acoustic communication focus on short units of vocalization such as songs, yet these units are often hierarchically organized into higher-order sequences and, outside human language, little is known about the drivers of sequence structure. Here, we investigate the organization, transmission and function of vocal sequences sung by male Albert’s lyrebirds (Menura alberti), a species renowned for vocal imitations of other species. We quantified the organization of mimetic units into sequences, and examined the extent to which these sequences are repeated within and between individuals and shared among populations. We found that individual males organized their mimetic units into stereotyped sequences. Sequence structures were shared within and to a lesser extent among populations, implying that sequences were socially transmitted. Across the entire species range, mimetic units were sung with immediate variety and a high acoustic contrast between consecutive units, suggesting that sequence structure is a means to enhance receiver perceptions of repertoire complexity. Our results provide evidence that higher-order sequences of vocalizations can be socially transmitted, and that the order of vocal units can be functionally significant. We conclude that, to fully understand vocal behaviours, we must study both the individual vocal units and their higher-order temporal organization.

Songbirds as a whole are considered to be vocal production learners, meaning that they modify the structure of their vocalizations as a result of experience with the vocalizations of others. The more than 4000 species of songbirds, however, vary greatly in crucial features of song development. Variable features include: (i) the normality of the songs of early-deafened birds, reflecting the importance of innate motor programmes in song development; (ii) the normality of the songs of isolation-reared birds, reflecting the combined importance of innate auditory templates and motor programmes; (iii) the degree of selectivity in choice of external models; (iv) the accuracy of copying from external models; and (v) whether or not learning from external models continues into adulthood. We suggest that because of this variability, some songbird species, specifically those that are able to develop songs in the normal range without exposure to external models, can be classified as limited vocal learners. Those species that require exposure to external models to develop songs in the normal range can be considered complex vocal learners. This article is part of the theme issue 'Vocal learning in animals and humans'.

Songbird syntax is generally thought to be simple, in particular lacking long-distance dependencies in which one element affects choice of another occurring considerably later in the sequence. Here, we test for long-distance dependencies in the sequences of songs produced by song sparrows (Melospiza melodia). Song sparrows sing with eventual variety, repeating each song type in a consecutive series termed a ‘bout’. We show that in switching between song types, song sparrows follow a ‘cycling rule’, cycling through their repertoires in close to the minimum possible number of bouts. Song sparrows do not cycle in a set order but rather vary the order of song types from cycle to cycle. Cycling in a variable order strongly implies longdistance dependencies, in which choice of the next type depends on the song types sung over the past cycle, in the range of 9–10 bouts. Song sparrows also follow a ‘bout length rule’, whereby the number of repetitions of a song type in a bout is positively associated with the length of the interval until that type recurs. This rule requires even longer distance dependencies that cross one another; such dependencies are characteristic of more complex levels of syntax than previously attributed to non-human animals.