Download Bird Song By Frank Edward

How many poets have listened to that birdsong? I cannot count and neither can you. The countlessness of that is magnificent! The wonder of all those poets and all that music is what this project is about. And so I write and post this new piece here this morning, tenaciously.

Ross Edwards AM (born 23 December 1943) is an Australian composer of a wide variety of music including orchestral and chamber music, choral music, children's music, opera and film music. His distinctive sound world reflects his interest in deep ecology and his belief in the need to reconnect music with elemental forces, as well as restore its traditional association with ritual and dance. He also recognises the profound importance of music as an agent of healing. His music, universal in that it is concerned with age-old mysteries surrounding humanity, is at the same time connected to its roots in Australia, whose cultural diversity it celebrates, and from whose natural environment it draws inspiration, especially birdsong and the mysterious patterns and drones of insects. As a composer living and working on the Pacific Rim, he is aware of the exciting potential of this vast region.[1][2]

Download 🔥 https://tiurll.com/2y4J1U 🔥

Human speech possesses a rich hierarchical structure that allows for meaning to be altered by words spaced far apart in time. Conversely, the sequential structure of nonhuman communication is thought to follow non-hierarchical Markovian dynamics operating over only short distances. Here, we show that human speech and birdsong share a similar sequential structure indicative of both hierarchical and Markovian organization. We analyze the sequential dynamics of song from multiple songbird species and speech from multiple languages by modeling the information content of signals as a function of the sequential distance between vocal elements. Across short sequence-distances, an exponential decay dominates the information in speech and birdsong, consistent with underlying Markovian processes. At longer sequence-distances, the decay in information follows a power law, consistent with underlying hierarchical processes. Thus, the sequential organization of acoustic elements in two learned vocal communication signals (speech and birdsong) shows functionally equivalent dynamics, governed by similar processes.

To determine whether hierarchical, Markovian, or some combination of these two processes better explain sequential dependencies in vocal communication signals, we measured the sequential dependencies between vocal elements in birdsong and human speech. Birdsong (i.e., the learned vocalizations of Oscine birds) is an attractive system to investigate common characteristics of communication signals because birds are phylogenetically diverse and distant from humans, but their songs are spectrally and temporally complex like speech, with acoustic units (notes, motifs, phrases, and bouts) spanning multiple timescales28. A number of complex sequential relationships have been observed in the songs of different species17,18,19,20,21,22,23,29. Most theories of birdsong sequential organization assume purely short timescale dynamics16,30,31,32, however, and rely typically on far smaller corpora than those available for written language. Because nonhuman species with complex vocal repertoires often produce hundreds of different vocal elements that may occur with exceptional rarity21, fully capturing the long-timescale dynamics in these signals is data intensive.

Collectively, our results reveal a common structure in both the short- and long-range sequential dependencies between vocal elements in birdsong and speech. For short timescale dependencies, information decay is predominantly exponential, indicating sequential structure that is governed largely by Markovian processes. Throughout vocal sequences, however, and especially for long timescale dependencies, a power law, indicative of non-Markovian hierarchical processes, governs information decay in both birdsong and speech.

These results change our understanding of how speech and birdsong are related. For speech, our observations of non-Markovian processes are not unexpected. For birdsong, they explain a variety of complex sequential dynamics observed in prior studies, including long-range organization20, music-like structure19, renewal processes17,18, and multiple timescales of organization23,29. In addition, the dominance of Markovian dynamics at shorter timescales may explain why such models have seemed appealing in past descriptions of birdsong28,30 and language43 which have relied on relatively small data sets parsed into short bouts (or smaller segments) where the non-Markovian structure is hard to detect (Supplementary Fig. 7). Because the longer-range dependencies in birdsong and speech cannot be fully explained by Markov models, our observations rule out the notion that either birdsong or speech is fully defined by regular grammars28. Instead, we suggest that the organizing principles of birdsong23, speech1, and perhaps sequentially patterned behaviors in general27,44, are better explained by models that incorporate hierarchical organization. The composite structure of the sequential dependencies in these signals helps explain why Hidden Markov Models (HMMs) and Recurrent Neural Networks (RNNs) have been used successfully to model sequential dynamics in speech3,45,46,47,48,49,50 and (to a lesser extent) animal communication29,32,51,52,53,54,55,56,57. HMMs are a class of Markov model which can represent hidden states that underlie observed data, allowing more complex (but still Markovian) sequential dynamics to be captured. HMMs have historically played an important role in speech and language-modeling tasks such as speech synthesis58 and speech recognition50, but have recently been overtaken by RNNs46,47,48,49,59, which model long-range dependencies better than the Markovian assumptions underlying HMMs. A similar shift to incorporate RNNs, or other methods to model hierarchical dynamics, will aid our understanding of at least some nonhuman vocal communication signals.

Our observations provide evidence that the sequential dynamics of human speech and birdsong are governed by both Markovian and hierarchical processes. Importantly, this result does not speak to the presence of any specific formal grammar underlying the structure of birdsong, especially as it relates to the various hierarchical grammars thought to support the phrasal syntax of language. It is possible that the mechanisms governing syntax are distinct from those governing other levels of hierarchical organization. One parsimonious conclusion is that the non-Markovian dynamics seen here are epiphenomena of a class of hierarchical processes used to construct complex signals or behaviors from smaller parts, as have been observed in other organisms including fruit flies60,61. These processes might reasonably be co-opted for speech and language production62. Regardless of variability in mechanisms, however, the power-law decay in information content between vocal elements is not unique to human language. It can and does occur in other temporally sequenced vocal communication signals including those that lack a well-defined (perhaps any) hierarchical syntactic organization through which meaning is conveyed.

As each of the data sets was transcribed using a different methodology, this disparity between the transcription methods may account for some differences in the observed MI decay. The impact of using different transcription methods are at present unknown. The same disparity is true of the birdsong data sets.

To determine a reasonable range of element-to-element distances for all the birdsong and speech data sets, we analyzed the relative goodness-of-fit (AICc) and proportion of variance explained (r2) for each model on decays over distances ranging from 15 to 1000 phones/syllables apart. The composite model provides the best fit for distances up to at least 1000 phones in each language (Supplementary Fig. 10) and at least the first 100 syllables for all songbird species (Supplementary Fig. 11). To keep analyses consistent across languages and songbird species we report on analyses using distances up to 100 elements (syllables in birdsong and phones in speech). Figures 3 and 4 show a longer range of decay in each language and songbird species, plotted up to element distances where the coefficient of determination (r2) remained within 99.9% of its value when fit to 100-element distances.

To address the possibility that repeating syllables might account for long-range order, we performed separate analyses on both the original syllable sequences (as produced by the bird) and compressed sequences in which all sequentially repeated syllables were counted as a single syllable. The original and compressed sequences show similar MI decay shapes (Supplementary Fig. 12). We also assessed how our results relate to the timescale of segmentation and discretization of syllables or phones by computing the decay in MI between discretized spectrograms of speech and birdsong at different temporal resolutions (Supplementary Fig. 13) for a subset of the data. Long-range relationships are present throughout both speech and birdsong regardless of segmentation, but the pattern of MI decay does not follow the hypothesized decay models as closely as that observed when the signals are discretized to phones or syllables, supporting the nonarbitrariness of these low-level production units.

We compared the MI decay of sequences produced by three different artificial grammars: (1) Markov models used to describe the song of two Bengalese finches31,32, (2) The hierarchical model proposed by Lin and Tegmark3, and (3) a model composed of both the hierarchical model advocated by Lin and Tegmark and a Markov model. While these models do not capture the full array of possible sequential models and their signatures in MI decay, they well-capture the predictions made based upon the discussed literature2,3,6,7,14 and provide an illustration of what would be expected given our competing hypotheses. With each model, we generate corpora of sequences, then compute the MI decay of the sequences using the same methods as with the birdsong and speech data. We also fit a power-law, exponential, and composite model to the MI decay, in the same manner (Fig. 2). e24fc04721