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A study of vocal nonlinearities in humpback whale songs: from production mechanisms to acoustic analysis - PubMed

  • ️Fri Jan 01 2016

A study of vocal nonlinearities in humpback whale songs: from production mechanisms to acoustic analysis

Dorian Cazau et al. Sci Rep. 2016.

Abstract

Although mammalian vocalizations are predominantly harmonically structured, they can exhibit an acoustic complexity with nonlinear vocal sounds, including deterministic chaos and frequency jumps. Such sounds are normative events in mammalian vocalizations, and can be directly traceable to the nonlinear nature of vocal-fold dynamics underlying typical mammalian sound production. In this study, we give qualitative descriptions and quantitative analyses of nonlinearities in the song repertoire of humpback whales from the Ste Marie channel (Madagascar) to provide more insight into the potential communication functions and underlying production mechanisms of these features. A low-dimensional biomechanical modeling of the whale's U-fold (vocal folds homolog) is used to relate specific vocal mechanisms to nonlinear vocal features. Recordings of living humpback whales were searched for occurrences of vocal nonlinearities (instabilities). Temporal distributions of nonlinearities were assessed within sound units, and between different songs. The anatomical production sources of vocal nonlinearities and the communication context of their occurrences in recordings are discussed. Our results show that vocal nonlinearities may be a communication strategy that conveys information about the whale's body size and physical fitness, and thus may be an important component of humpback whale songs.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1

Distributions of nonlinear measures ϕfj and ϕch, respectively on the left and on the right, in four different two-parameter bifurcation diagrams: left-right U-fold asymmetry formula image/lung pressure formula image (diagram A), U-fold sizing formula image/lung pressure formula image (diagram B), size of the nasal cavities formula image/size of the laryngeal sac formula image (diagrams C,D). The formula image represents parameter values normalized by their nominal values from Table 3. The simulated vocalizations of diagram C have been obtained with an upward fundamental frequency modulation, and those of diagram D have been obtained with a laryngeal sac extension.

Figure 2
Figure 2. Examples of sound units with frequency jumps.

The sound units (A–C,E,F) contain a box indicating the location of frequency jumps, with y-axis values. The sound unit D is short enough so we can distinctly visualize the frequency jump. Spectrogram parameters: sampling rate Fs: 44.1 kHz, frame size: 22 ms (1024 samples), 50% overlap (temporal resolution: 11 ms), FFT size: 1024 samples (spectral resolution: 11 Hz), Hamming window.

Figure 3
Figure 3. Examples of temporal distribution of chaotic segments in various nonlinear vocalizations.

The solid black lines represent the ϕch curves. Spectrogram parameters: sampling rate Fs: 44.1 kHz, frame size: 22 ms (1024 samples), 50% overlap (temporal resolution: 11 ms), FFT size: 1024 samples (spectral resolution: 11 Hz), Hamming window.

Figure 4
Figure 4

Temporal evolution of the descriptors ϕfj and ϕch over four different songs (A–D) (see Table 2 for details). The difference in linewidths between the graphs is explained by their different number of sound units, from 1223 (in song A) to 116 (in song B).

Figure 5
Figure 5. Description of the U-fold modeling.

(A) Photo of the U-fold, with a two-mass system drawn it. (B) Area function of sound production system of humpback whales. This area function is not anatomically accurate, as air flow makes a U-turn above the trachea towards the laryngeal sac (see Reidenberg and Laitman and Cazau et al. for details). However, this turn is supposed not to play a role in our sound production model. (C) Two-mass model scheme. On each of these subfigures, the layngeal region is framed in blue.

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