An inverse relationship between body size and advertisement call frequency has been found in several frog species. However, the generalizability of this relationship across different clades and across a large distribution of species remains underexplored. We investigated this relationship in a large sample of 136 species belonging to four clades of anurans (Bufo, Hylinae, Leptodactylus and Rana) using semi‐automatic, high‐throughput analysis software. We employed two measures of call frequency: fundamental frequency (F0) and dominant frequency (DF). The slope of the relationship between male snout‐vent length (SVL) and frequency did not differ significantly among the four clades. However, Rana call at a significantly lower frequency relative to size than the other clades, and Bufo call at a significantly higher frequency relative to size than Leptodactylus. Because the relationship between F0 and body size may be more straightforwardly explained by biomechanical constraints, we confirmed that a similar inverse relationship was observed between F0 and SVL. Finally, spectral flatness, an indicator of the tonality of the vocalizations, was found to be inversely correlated with SVL, contradicting an oft‐cited prediction that larger animals should have rougher voices. Our results confirm a tight and widespread link between body size and call frequency in anurans, and suggest that laryngeal allometry and vocal fold dimensions in particular are responsible.

Anuran vocalizations, especially their advertisement calls, are largely species-specific and can be used to identify taxonomic affiliations. Because anurans are not vocal learners, their vocalizations are generally assumed to have a strong genetic component. This suggests that the degree of similarity between advertisement calls may be related to large-scale phylogenetic relationships. To test this hypothesis, advertisement calls from 90 species belonging to four large clades (Bufo, Hylinae, Leptodactylus, and Rana) were analyzed. Phylogenetic distances were estimated based on the DNA sequences of the 12S mitochondrial ribosomal RNA gene, and, for a subset of 49 species, on the rhodopsin gene. Mean values for five acoustic parameters (coefficient of variation of root-mean-square amplitude, dominant frequency, spectral flux, spectral irregularity, and spectral flatness) were computed for each species. We then tested for phylogenetic signal on the body-size-corrected residuals of these five parameters, using three statistical tests (Moran’s I, Mantel, and Blomberg’s K) and three models of genetic distance (pairwise distances, Abouheif’s proximities, and the variance-covariance matrix derived from the phylogenetic tree). A significant phylogenetic signal was detected for most acoustic parameters on the 12S dataset, across statistical tests and genetic distance models, both for the entire sample of 90 species and within clades in several cases. A further analysis on a subset of 49 species using genetic distances derived from rhodopsin and from 12S broadly confirmed the results obtained on the larger sample, indicating that the phylogenetic signals observed in these acoustic parameters can be detected using a variety of genetic distance models derived either from a variable mitochondrial sequence or from a conserved nuclear gene. We found a robust relationship, in a large number of species, between anuran phylogenetic relatedness and acoustic similarity in the advertisement calls in a taxon with no evidence for vocal learning, even after correcting for the effect of body size. This finding, covering a broad sample of species whose vocalizations are fairly diverse, indicates that the intense selection on certain call characteristics observed in many anurans does not eliminate all acoustic indicators of relatedness. Our approach could potentially be applied to other vocal taxa.

The possibility of achieving experimentally controlled, non-vocal acoustic production in non-human primates is a key step to enable the testing of a number of hypotheses on primate behavior and cognition. However, no device or solution is currently available, with the use of sensors in non-human animals being almost exclusively devoted to applications in food industry and animal surveillance. Specifically, no device exists which simultaneously allows: (i) spontaneous production of sound or music by non-human animals via object manipulation, (ii) systematical recording of data sensed from these movements, (iii) the possibility to alter the acoustic feedback properties of the object using remote control. We present two prototypes we developed for application with chimpanzees (Pan troglodytes) which, while fulfilling the aforementioned requirements, allow to arbitrarily associate sounds to physical object movements. The prototypes differ in sensing technology, costs, intended use and construction requirements. One prototype uses four piezoelectric elements embedded between layers of Plexiglas and foam. Strain data is sent to a computer running Python through an Arduino board. A second prototype consists in a modified Wii Remote contained in a gum toy. Acceleration data is sent via Bluetooth to a computer running Max/MSP. We successfully pilot tested the first device with a group of chimpanzees. We foresee using these devices for a range of cognitive experiments.

The vocalizations of anurans are innate in structure and may therefore contain indicators of phylogenetic history. Thus, advertisement calls of species which are more closely related phylogenetically are predicted to be more similar than those of distant species. This hypothesis was evaluated by comparing several widely used machine-learning algorithms. Recordings of advertisement calls from 142 species belonging to four genera were analyzed. A logistic regression model, using mean values for dominant frequency, coefficient of variation of root-mean square energy, and spectral flux, correctly classified advertisement calls with regard to genus with an accuracy above 70%. Similar accuracy rates were obtained using these parameters with a support vector machine model, a K-nearest neighbor algorithm, and a multivariate Gaussian distribution classifier, whereas a Gaussian mixture model performed slightly worse. In contrast, models based on mel-frequency cepstral coefficients did not fare as well. Comparable accuracy levels were obtained on out-of-sample recordings from 52 of the 142 original species. The results suggest that a combination of low-level acoustic attributes is sufficient to discriminate efficiently between the vocalizations of these four genera, thus supporting the initial premise and validating the use of high-throughput algorithms on animal vocalizations to evaluate phylogenetic hypotheses. (C) 2013 Acoustical Society of America. [http://dx.doi.org/10.1121/1.4768878]

Can listeners distinguish unfamiliar performers playing the same piece on the same instrument? Professional performers recorded two expressive and two inexpressive interpretations of a short organ piece. Nonmusicians and musicians listened to these recordings and grouped together excerpts they thought had been played by the same performer. Both musicians and nonmusicians performed significantly above chance. Expressive interpretations were sorted more accurately than inexpressive ones, indicating that musical individuality is communicated more efficiently through expressive performances. Furthermore, individual performers' consistency and distinctiveness with respect to expressive patterns were shown to be excellent predictors of categorisation accuracy. Categorisation accuracy was superior for prize-winning performers compared to non-winners, suggesting a link between performer competence and the communication of musical individuality. Finally, results indicate that temporal information is sufficient to enable performer recognition, a finding that has broader implications for research on the detection of identity cues.