So. Murray et al., The neural network classification of false killer whale (Pseudorca crassidens) vocalizations, J ACOUST SO, 104(6), 1998, pp. 3626-3633
This study reports the use of unsupervised, self-organizing neural network
to categorize the repertoire of false killer whale vocalizations. Self-orga
nizing networks are capable of-detecting patterns in their input and partit
ioning 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 measuremen
ts of duty cycle and peak frequency. The first neural network used competit
ive learning, where units in a competitive layer distributed themselves to
recognize frequently presented input vectors. This network resulted in clas
ses representing typical patterns in the vocalizations. The second network
was a Kohonen feature map which organized the outputs topologically, provid
ing a graphical organization of pattern relationships. The networks perform
ed well as measured by (1) the average correlation between the input vector
s and the weight vectors for each category, and (2) the ability of the netw
orks to classify novel vocalizations. The techniques used in this study cou
ld easily be applied to other species and facilitate the development of obj
ective, comprehensive repertoire models. (C) 1998 Acoustical Society of Ame
rica. [S0001-4966(98)03312-8].