The Process of Song-Learning in the Chaffinch as Studied by Means of the Sound Spectrograph

Thorpe, W. H.

doi:10.1038/173465a0

Cited by 140 publications

(56 citation statements)

References 5 publications

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“…Categorizations produced by humans are traditionally based on spectrographic representations of the acoustic signal (Thorpe, 1954). Three analysis methods commonly used for this purpose are visual comparison of the spectrograms by a trained experimenter (Janik, 1999;Bloomfield et al, 2004), spectrographic cross-correlation (Khanna et al, 1997;Janik, 1999;Cortopassi and Bradbury, 2000;Baker, 2003), and extraction of acoustic features in the signal (Nowicki and Nelson, 1990;Charrier et al, 2004;Draganoiu et al, 2006).…”

Section: A Analysis Methods Of Animal Vocalizationsmentioning

confidence: 99%

Finding good acoustic features for parrot vocalizations: The feature generation approach

Giret

Roy²,

Albert

et al. 2011

The Journal of the Acoustical Society of America

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A crucial step in the understanding of vocal behavior of birds is to be able to classify calls in the repertoire into meaningful types. Methods developed to this aim are limited either because of human subjectivity or because of methodological issues. The present study investigated whether a feature generation system could categorize vocalizations of a bird species automatically and effectively. This procedure was applied to vocalizations of African gray parrots, known for their capacity to reproduce almost any sound of their environment. Outcomes of the feature generation approach agreed well with a much more labor-intensive process of a human expert classifying based on spectrographic representation, while clearly out-performing other automated methods. The method brings significant improvements in precision over commonly used bioacoustical analyses. As such, the method enlarges the scope of automated, acoustics-based sound classification.

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Section: A Analysis Methods Of Animal Vocalizationsmentioning

confidence: 99%

Finding good acoustic features for parrot vocalizations: The feature generation approach

Giret

Roy²,

Albert

et al. 2011

The Journal of the Acoustical Society of America

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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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“…Furthermore, juveniles raised in acoustic isolation from other birds develop songs lacking many of the fine variations in pitch and duration that typify wild-type songs (Thorpe 1954(Thorpe , 1958Immelmann 1969;Marler 1970;Marler et al 1972;Price 1979). Finally, in many instances, young songbirds can copy the song of another species (Immelmann 1969;Petrinovich 1984, 1986;Marler 1991).…”

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Neural mechanisms for learned birdsong

2009

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Learning by imitation is essential for transmitting many aspects of human culture, including speech, language, art, and music. How the human brain enables imitation remains a mystery, but the underlying neural mechanisms must harness sensory feedback to adaptively modify performance in reference to the object of imitation. Although examples of imitative learning in nonhuman animals are relatively rare, juvenile songbirds learn to sing by copying the song of an adult tutor. The delineation of neural circuits for birdsong raises the promise that this complex form of vocal learning, which bears strong parallels to human speech learning, can be understood in terms of underlying neural mechanisms. This promise is now being more fully realized, with recent experimental advances leading to better understanding of the central motor codes for song and the central mechanisms by which auditory experience modifies song motor commands to enable vocal learning.

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The Process of Song-Learning in the Chaffinch as Studied by Means of the Sound Spectrograph

Cited by 140 publications

References 5 publications

Finding good acoustic features for parrot vocalizations: The feature generation approach

Finding good acoustic features for parrot vocalizations: The feature generation approach

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

Neural mechanisms for learned birdsong

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