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Although much research has been done to describe the degradation of sound signals propagating in natural habitats, the directional cues of sound have so far been neglected. This paper describes a first approach to quantifying the degradation of directional cues in sound propagating parallel to the ground in a grassland habitat of orthopteran insects. A matched pair of probe microphones measured the sound amplitude and phase close to the ears of grasshopper carcasses for 12 evenly spaced directions of sound incidence. The degradation was found to increase with frequency and distance from the sound source and to decrease with distance from the ground. The acoustical data were used to predict how well animals with different auditory systems can determine the direction of the sender. At one position in the habitat, the predictions were compared with the pattern of phonotactic responses of live grasshoppers. Amplitude cues appear to degrade much faster with distance than phase cues. Animals exploiting phase cues may therefore maintain a reasonable directional hearing when the amplitude cues no longer make sense. The pressure-difference-receiver type of ears responds to phase differences, and these ears may be particularly suited to overcoming the degradation of directional cues. This suggests that the possession of such ears may be an adaptation not only to small body size (relative to wavelength), but also to the acoustic properties of the habitat

The voices of 8 hinds and their 7 offspring were analysed spectrographically. They were transformed into sonograms and the following parameters were measured: duration, fundamental frequency and frequency bands with high amplitudes. The animals emitted single calls or a series of sounds at irregular intervals. The single calls were monosyllabic and the series were homotypical sequences. The hind voices were deep and bleatlike, ranging in frequency from 70 to 3000 Hz. The fundamental frequency was 108.35 + 15.21 Hz and the duration was 0.27 + 0.14 sec. There were 1 to 8 frequency bands with high amplitudes created. Differences between individual voices were found in all characteristics of vocalization tested. The variation between voices was a result of the combination of these characteristics. The most important factor seemed to be the pattern of main frequency bands with high amplitudes. The calf voices were high, whiny and tonal, ranging in frequency from 320 to 7000 Hz. The fundamental frequency was 736.97 + 177.67 Hz and the duration was 0.26 + 0.12 sec. They were similar to each other, and inter-individual variation was not very apparent. This suggests that while hinds can be recognized individually by voice, it is probably not possible to distinguish the calves by voice alone. This is in accordance with parallel findings, that hinds did not seem to recognise their own calf’s voice.

A unique up-slurred call of the willow tit Parus montanus, rendered pui or plui, was discovered at a locality in the birch alpine region of central Norway in the summer of 1987 when three neighbouring pairs shared this call. Up to 1996 inclusive an additional number of 19 pui-calling individuals were found. Typically, these birds were either offspring of birds already uttering the call, or members of winter flocks in which the dominant adults possessed the call. The call is evidently acquired by learning and serves as an alternative alarm call. It is suggested that rudimentary pui-calls are a normal component in babbling series of young willow tits in general and that the development of these into full calls depends on appropriate tutors. It should be emphasized that the pui-call does not replace another call in the repertoire, but is an extra call adding to the repertoire. It is probably the first time that such a phenomenon has been reported in birds. The expectation that the call would spread in the population has so far proved wrong.

The computer-aided analysis of acoustic signals of mammals is still a problem, as often (a) sound structures are complex, (b) vocal repertoires often comprise an enormous variety of vocalisations, (c) recordings are influenced by the acoustic conditions of the environment, and (d) the distance and spatial orientation of the sender to the microphone changes. In recent software packages for the analysis of acoustic signals, procedures are integrated which allow the calculation of a variety of signal features. However, these algorithms are often problematic under the conditions mentioned above. In this paper, we present a multi-parametric approach which reduces these problems and which allows a quantitative and reproducible analysis of complex animal vocalisations. Our approach comprises the following aspects: (1) reduction of influences of recording conditions, (2) determination of different sound features and (3) calculation of parameters to characterize these sound features. All calculations are done on the basis of the digitized spectrograms. Special attention is given to the use of smoothing algorithms and dynamic thresholds in order to estimate sound features and to reduce influences resulting from recording conditions. The suitability of our approach has been demonstrated successfully for vocalisations of different species.

Digital spectrographic cross-correlation (SPCC), a technique described by Clark et a1. (1987), simultaneously analyses frequency, amplitude and time components of a signal, and returns a single peak correlation coefficient. The procedure is objective and uses all the information in the spectrogram. As such, it is a candidate to replace and/or supplement visual spectrogram comparison and multivariate analysis as the technique of choice for comparing sounds. With the increasing availability of sound analysis software with built-in cross-correlation routines, the procedure is becoming readily available to biologists who may not have extensive knowledge of acoustics. This ease of access increases the potential for misapplication of the technique or misinterpretation of results. To assess the utility of SPCC and to highlight pitfalls that need to be avoided in its implementation, we performed a series of tests designed to reveal the sensitivity of the peak cross-correlation coefficient to a variety of parameters. In general, SPCC provides a good measure of the similarity between simple, continuous signals. However, there is no single best measure of similarity because of the complementarity of frequency and time resolution. More complex signal structures, such as those with overtones or complex vocalizations, will often return misleading coefficients. In all cases, pre-test preparation of signals is of critical importance.