| Abstract: | In an attempt to minimize observer bias, numerical taxonomy methods were used to describe and classify humpback whale sounds. The spectrograms (N = 1255) were digitized into a 16 × 21 binary matrix. The rows were 16 frequencies selected on a logarithmic scale (0.12–8 kHz). The columns were 21 time samples taken every 0.1 s. Each point of the matrix was coded 1 if it lay over part of the sound. Other binary variables were added to code for relative intensity within a sound, frequency modulation and amplitude modulation. The sounds were then compared using the Jaccard similarity coefficient for binary data, and classified with average linkage cluster analysis. This technique produced 115 clusters, which were compared with my aural and visual impressions of the sounds. I agreed with most major categories identified by cluster analysis, but many small clusters had to be fused to other categories. This was partially due to the technique used, and to the complexity of the repertoire under study. Improvements are proposed to further reduce observer bias in classification of sounds, and thus make studies of animal communication performed by different researchers or on different species more easily comparable. |
