A segmentation algorithm for zebra finch song at the note level

Du, Ping; Troyer, Todd W.

doi:10.1016/j.neucom.2005.12.110

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…Vocal elements were defined as continuous sounds bounded by silent periods. Thresholding the amplitudes of the pressure waves is a common approach of isolating vocal elements in birdsongs [31] , [41] , [42] . We developed a similar method.…”

Section: Methodsmentioning

confidence: 99%

A Compact Statistical Model of the Song Syntax in Bengalese Finch

2011

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Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively, and allows associations of more than one state to a given syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC, but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model.

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Section: Methodsmentioning

confidence: 99%

A Compact Statistical Model of the Song Syntax in Bengalese Finch

2011

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“…The sonogram (Fig. 1A) (2)(3)(4) provides an excellent descriptive model of singing behavior, and over the past decade several analysis approaches were developed that allow automatic segmentation categorization and comparison of vocal sounds (5)(6)(7)(8)(9). In this review, we describe the role of behavioral analysis in the progress made in birdsong neuroethology research.…”

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confidence: 99%

Quantification of developmental birdsong learning from the subsyllabic scale to cultural evolution

Lipkind

Tchernichovski

2011

Proc. Natl. Acad. Sci. U.S.A.

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Quantitative analysis of behavior plays an important role in birdsong neuroethology, serving as a common denominator in studies spanning molecular to system-level investigation of sensory-motor conversion, developmental learning, and pattern generation in the brain. In this review, we describe the role of behavioral analysis in facilitating cross-level integration. Modern sound analysis approaches allow investigation of developmental song learning across multiple time scales. Combined with novel methods that allow experimental control of vocal changes, it is now possible to test hypotheses about mechanisms of vocal learning. Further, song analysis can be done at the population level across generations to track cultural evolution and multigenerational behavioral processes. Complementing the investigation of song development with noninvasive brain imaging technology makes it now possible to study behavioral dynamics at multiple levels side by side with developmental changes in brain connectivity and in auditory responses.B irdsong neuroethology is a small but diverse and influential subfield of neuroscience, studied in ∼100 laboratories, and covering a range of research areas spanning the molecular to the organismal levels of investigation. The quantification of singing behavior has been a common denominator across most of those studies. Over the past four decades, birdsong neuroethology has made tremendous gains by incorporating existing tools for representing and analyzing sound (1). The sonogram (Fig. 1A) (2-4) provides an excellent descriptive model of singing behavior, and over the past decade several analysis approaches were developed that allow automatic segmentation categorization and comparison of vocal sounds (5-9). In this review, we describe the role of behavioral analysis in the progress made in birdsong neuroethology research.The past 20 y or so have seen rapid progress in understanding the neural and molecular processes involved in song. However, this progress was preceded by a long tradition of quantifying singing behavior (10, 11). Birdsong is uniquely amenable and attractive for behavioral analysis for a number of reasons: adult song is a structured behavior, repeated with a high degree of stereotypy (Fig. 1A). This makes it relatively easy to detect and characterize song structure and components (e.g., notes, syllables, motifs, and phrases) to align song renditions to each other, to assess the degree of similarity between songs, and, ultimately, to relate singing behavior to neuronal events and thus elucidate underlying neural mechanisms. We start by reviewing the role of behavioral quantification in achieving integration across levels of investigation. We then present a short outline of sound analysis techniques currently used for the tracking of developmental song learning, and an experimental approach for hypothesis-driven investigation of song development. Finally, we present a generalization of behavioral analysis to the population and cross-generation levels, and propose some ideas about n...

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“…This volume of data represents a methodological challenge because most bioacoustic workflows require the songs to be segmented [11]. This segmentation necessitates identifying sounds of the target species from noisy backgrounds (e.g., recordings taken in windy places or areas with anthropogenic noise), from nontarget species, or, for more complex analyses, identifying individual syllables of song [e.g., [25][26][27][28][29]. To date, this process still requires careful tuning from researchers.…”

Section: Avian Bioacousticsmentioning

confidence: 99%

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

2022

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Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

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A segmentation algorithm for zebra finch song at the note level

Cited by 7 publications

References 8 publications

A Compact Statistical Model of the Song Syntax in Bengalese Finch

A Compact Statistical Model of the Song Syntax in Bengalese Finch

Quantification of developmental birdsong learning from the subsyllabic scale to cultural evolution

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

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