What is vocology?

In its broadest definition, Vocology is the study of vocalization, much like audiology is the study of hearing. Vocology includes the exploration of the full capability of human and animal sound production, some of which is embedded in human speech. For professional practice, a secondary definition of Vocology is the science and practice of voice habilitation, concept that has been in existence for more than two decades. The emphasis is on habilitation rather than re-habilitation, so that the field does not infringe on speech-language pathology. Besides, it does include the important area of animal vocalization.

Birdsong fails to support object categorization in human infants

Recent evidence reveals a precocious link between language and cognition in human infants: listening to their native language supports infants’ core cognitive processes, including object categorization, and does so in a way that other acoustic signals (e.g., time-reversed speech; sine-wave tone sequences) do not. Moreover, language is not the only signal that confers this cognitive advantage: listening to vocalizations of non-human primates also supports object categorization in 3- and 4-month-olds. Here, we move beyond primate vocalizations to clarify the breadth of acoustic signals that promote infant cognition. We ask whether listening to birdsong, another naturally produced animal vocalization, also supports object categorization in 3- and 4-month-old infants. We report that listening to zebra finch song failed to confer a cognitive advantage. This outcome brings us closer to identifying a boundary condition on the range of non-linguistic acoustic signals that initially support infant cognition.

Animal Sound Classification Using Dissimilarity Spaces

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using four different backbones with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. The proposed approach uses clustering methods to determine a set of centroids (in both a supervised and unsupervised fashion) from the spectrograms in the dataset. Such centroids are exploited to generate the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, experiments process the spectrograms using the heterogeneous auto-similarities of characteristics. Once the similarity spaces are computed, each pattern is “projected” into the space to obtain a vector space representation; this descriptor is then coupled to a support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best standalone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset.

Animal Sound Classification Using Dissimilarity Spaces

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using 4 different backbones, with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. Different clustering methods reduce the spectrograms in the dataset to a set of centroids that generate (in both a supervised and unsupervised fashion) the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, additional experiments process the spectrograms using the Heterogeneous Auto-Similarities of Characteristics. Once the similarity spaces are computed, a vector space representation of each pattern is generated that is then trained on a Support Vector Machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best stand-alone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset. The MATLAB code used in this study is available at https://github.com/LorisNanni.

Howling by the river: howler monkey (Alouatta palliata) communication in an anthropogenically-altered riparian forest in Costa Rica

The ways that forest edges may affect animal vocalization behaviour are poorly understood. We investigated the effects of various types of edge habitat on the loud calls (howls) of a folivorous-frugivorous primate species, Alouatta palliata, with reference to the ecological resource defence hypothesis, which predicts that males howl to defend vegetation resources. We tested this hypothesis across four forest zones — interior, riparian, anthropogenic, and combined forest edges — in a riparian forest fragment in Costa Rica. We predicted vegetation and howling would differ between forest zones, with riparian and interior zones showing the highest values and anthropogenic edge the lowest. Our results indicated that vegetation was richer and howling longer in riparian and interior zones compared to combined and anthropogenic edges, supporting the resource defence hypothesis and providing some of the first evidence in animal communication scholarship for differences in behavioural edge effects between natural riparian and anthropogenic edges.