Subject: Abstracts: false killer whale acoustics (fwd)

Mike Williamson (
Mon, 28 Dec 1998 16:44:34 -0500 (EST)

Forwarded Message From BIOACOUSTICS-L:

From:          Scott Murray <>

The following two papers were published recently in the Journal of
Acoustical Society of America.  Reprint requests should be directed to:

Scott Murray
Inst. of Theoretical Dynamics
1 Shields Ave.
UC Davis
Davis, CA 95616

Murray, S.O., Mercado, E., & Roitblat, H.L. (1998) Characterizing the
graded structure of false killer whale (Pseudorca crassidens)
vocalizations. Journal of the Acoustical Society of America, 104, pp.

The vocalizations from two, captive false killer whales (Pseudorca
crassidens) were analyzed. The structure of the vocalizations was best
modeled as lying along a continuum with trains of discrete, exponentially
damped sinusoidal pulses at one end and continuous sinusoidal signals at
the other end. Pulse trains were graded as a function of the interval
between pulses where the minimum interval between pulses could be zero
milliseconds. The transition from a pulse train with no inter-pulse
interval to a whistle could be modeled by gradations in the degree of
damping. There were many examples of vocalizations that were gradually
modulated from pulse trains to whistles.  There were also vocalizations
that showed rapid shifts in signal type--for example, switching
immediately from a whistle to a pulse train. These data have implications
when considering both the possible function(s) of the vocalizations and
the potential sound production mechanism(s). A short-time duty cycle
measure was developed to characterize the graded structure of the
vocalizations. A random sample of 500 vocalizations was characterized by
combining the duty cycle measure with peak frequency measurements. The
analysis method proved to be an effective metric for describing the graded
structure of false killer whale vocalizations.

Murray, S.O., Mercado, E., & Roitblat, H.L. (1998) The neural network
classification of false killer whale (Pseudorca crassidens) vocalizations.
Journal of the Acoustical Society of America, 104, pp. 3626-3633.

This study reports the use of unsupervised, self-organizing neural network
to categorize the repertoire of false killer whale vocalizations.
Self-organizing networks are capable of detecting patterns in their input
and partitioning those patterns into categories without requiring that the
number or types of categories be predefined. The inputs for the neural
networks were two-dimensional characterization of false killer whale
vocalizations, where each vocalization was characterized by a sequence of
short-time measurements of duty cycle and peak frequency. The first neural
network used competitive learning, where units in a competitive layer
distributed themselves to recognize frequently presented input vectors.
This network resulted in classes representing typical patterns in the
vocalizations. The second network was a Kohonen feature map which
organized the outputs topologically, providing a graphical organization of
pattern relationships. The networks performed well as measured by (1) the
average correlation between the input vectors and the weight vectors for
each category, and (2) the ability of the networks to classify novel
vocalizations. The techniques used in this study could easily be applied
to other species and facilitate the development of objective,
comprehensive repertoire models.

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