Animal Cognition

, Volume 19, Issue 2, pp 285–315 | Cite as

The vocal repertoire of the domesticated zebra finch: a data-driven approach to decipher the information-bearing acoustic features of communication signals

Original Paper

Abstract

Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter).

Keywords

Vocalization Songbird Acoustic signature Meaning Classification Regularization 

Notes

Acknowledgments

We would like to dedicate this study to Peter Marler and Richard Zann and Allison Doupe. By his fundamental discoveries and his thoughtful contributions to the field of animal communication, Peter Marler has been a major source of guidance and inspiration for our own research efforts in the field of bird communication. In his seminal Science paper in 1967, Peter Marler said: “We are beginning to understand how the structure of animal signals relates to the function they serve.” We would hope that Peter would agree that we are humbly following his footsteps. Peter Marler is the scientific great-grandparent of FET and great–great-grandparent of JEE. Richard Zann dedicated his life to the study of wild zebra finches in his native Australia. Allison Doupe developed the zebra finch model in crucial seminal studies that examined the neural mechanism of vocal plasticity. She was the scientific parent of FET and grandparent of JEE. She was an outstanding mentor and a wonderful person. We would not be able to appreciate the complexity and the relevance of our studies without their respective contributions to the field. Richard Zann died in a bushfire inferno that occurred in outskirts of Melbourne in February 2009. Peter Marler died in July 2014 following a long illness. Allison Doupe died in September 2014 after a long battle with cancer. This work was supported by an NIH grant CD010132 to FET, a CRCNS NSF grant IIS1311446 to FET and JEE and a fellowship from the Fyssen Foundation to JEE.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All applicable international, national and/or institutional guidelines for the care and use of animals were followed. All animal procedures were approved by the Animal Care and Use Committee of the University of California Berkeley and were in accordance with the NIH guidelines regarding the care and use of animals for experimental procedures.

Supplementary material

10071_2015_933_MOESM1_ESM.pdf (535 kb)
Supplementary Figure 1. Flow-chart showing the calculation of the modulation power spectrum (MPS) and the use of this acoustical representation in the classification procedure. In this feature space each sound is characterized by its MPS. The MPS is the amplitude square of the 2D Fourier Transform (2D FT) of the log spectrogram. The spectrogram was estimated with the same time-frequency scale as in Figure 2. The modulation power spectrum was sampled every 2.85 Hz between -40Hz and 40 Hz for temporal modulations (x-axis) and every 0.0826 cyc/kHz between 0 and 4 cyc/kHz for spectral modulations (y-axis) for a total of 1,500 parameters. As for the spectrographic representation, principal component analysis (PCA) was used to reduce the number of parameters to 40 before classification. The 40 parameters captured 34% of the variance in the modulation power spectrum across all vocalizations in our data set. The classifiers were trained to estimate the vocalization category (PDF 535 kb)
10071_2015_933_MOESM2_ESM.pdf (502 kb)
Supplementary Figure 2. Flow-chart showing the calculation of the Mel frequency cepstral coefficients (MFCC) and the use of this acoustical representation in the classification procedure. In this feature space each sound is characterized by a time sequence of cepstral coefficients. The cepstrum coefficients were obtained from the discrete cosine transform (DCT) of the log of the amplitude in one time slice of the spectrogram. For MFCC, the Mel spectrogram was obtained using 25 filterbank channels approximately logarithmically spaced (Mel spaced frequency bands) between 500 and 8000 Hz. The time windows were 25 ms long and spaced every 10 ms (15 ms overlap). Twelve cepstral indexes (ci) were extracted from each spectral envelope resulting in a 12 ci for 33 time points resulting in a total of 396 parameters. Similar to the spectrographic representation, principal component analysis (PCA) was used to reduce the number of parameters to 40 before classification. The 40 parameters captured 96% of the variance in the MFCC modulation power spectrum across all vocalizations in our dataset. The classifiers were trained to estimate the vocalization category (PDF 503 kb)
10071_2015_933_MOESM3_ESM.eps (633 kb)
Supplementary Figure 3. Spectrograms of three example calls exhibiting a double voice component. Double voices or two pitches were regularly found in zebra finch vocalizations. Here, are shown examples of a Whine, Nest and Begging call where the double voice can clearly be observed on the spectrogram. The arrows show the fundamental or harmonic corresponding to the two voices (EPS 632 kb)
10071_2015_933_MOESM4_ESM.pdf (595 kb)
Supplementary Figure 4. Unsupervised Clustering ofTetcalls: Sexual dimorphism and two types of calls. A “mixture-of-Gaussians” model was used to perform unsupervised clustering of groups of calls as described in the legend of Figure 9 and methods. A. Unsupervised clustering of all Tet calls produced by male and female birds resulted in a distribution well fitted by two Gaussians of approximately equal weight (w1=0.38, w2 = 0.62). Assignment to one of the two clusters resulted in significantly different proportions of male and female calls in each group (z = 6.72, P < 10-4) as illustrated on the bar plot on the right column. B and C. Unsupervised clustering of female (B) and male (C) Tet calls only. These distributions were also well fitted by two Gaussians of approximately equal weight (Female: w1=0.53, w2 = 0.47; Male: w1=0.31, w2 = 0.69). The color code on the scatter plots indicates vocalizers’ identity and show that individuals produce calls in each group although some produce mostly one “type”. Note that the mixture-of-Gaussians algorithm is blind to the vocalizer’s identity. We estimated mean values of each acoustical parameter for Tet calls assigned to each group and show the results for the CV of the fundamental (CV F0), the spectral mean (mean S), the duration (std T) and the intensity (RMS) with bar plots on the right panels. Error bars correspond to one sem. The most distinguishing acoustical feature is the CV of the fundamental (Female: t(325)=-14.71 P < 10-4; Male: t(284)=-11.93 P < 10-4) that for both sexes is much lower in one of the groups (group 1 for both). This group of calls with very low modulation of their fundamental has been described as Stacks (Ter Maat et al., 2014). Note that we re-estimated principal components for male and female calls only and therefore the PC axes correspond to different combination of acoustical features in all three rows (PDF 596 kb)
10071_2015_933_MOESM5_ESM.eps (1.7 mb)
Supplementary Figure 5. Unsupervised Clustering ofNestcalls: a unimodal distribution. A “mixture-of-Gaussians” model was used to perform unsupervised clustering of Nest calls as described in the legend of Figure 9 and methods. Although the BIC values suggest that this distribution is better fitted with two Gaussians than one Gaussian, the weights of these two Gaussians is greatly biased towards one group (w1=0.16, w2 = 0.84) demonstrating that the distribution is clearly unimodal, albeit not perfectly Gaussian (EPS 1767 kb)
10071_2015_933_MOESM6_ESM.eps (635 kb)
Supplementary Figure 6. Power Spectrum and Temporal Envelope forTetandDistancecalls.Tet (light purple) and Distance calls (dark purple) are the two calls that show sexual differences (solid male, dotted female). The left panel (A) shows the non-normalized frequency spectra and the right panel (B) the normalized temporal amplitude envelope (right) for these two calls and for male and female birds. In the power spectrum, one can also appreciate the shifts in the formant frequencies between Tet and Distance calls (EPS 635 kb)
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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Psychology and Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyUSA

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