Although the domestic cat (Felis catus) is probably the most widespread companion animal in the world and interacts in a complex and multifaceted way with humans, the human–cat relationship and reciprocal communication have received far less attention compared, for example, to the human–dog relationship. Only a limited number of studies have considered what people understand of cats’ human-directed vocal signals during daily cat–owner interactions. The aim of the current study was to investigate to what extent adult humans recognize cat vocalizations, namely meows, emitted in three different contexts: waiting for food, isolation, and brushing. A second aim was to evaluate whether the level of human empathy toward animals and cats and the participant’s gender would positively influence the recognition of cat vocalizations. Finally, some insights on which acoustic features are relevant for the main investigation are provided as a serendipitous result. Two hundred twenty-five adult participants were asked to complete an online questionnaire designed to assess their knowledge of cats and to evaluate their empathy toward animals (Animal Empathy Scale). In addition, participants had to listen to six cat meows recorded in three different contexts and specify the context in which they were emitted and their emotional valence. Less than half of the participants were able to associate cats’ vocalizations with the correct context in which they were emitted; the best recognized meow was that emitted while waiting for food. Female participants and cat owners showed a higher ability to correctly classify the vocalizations emitted by cats during brushing and isolation. A high level of empathy toward cats was significantly associated with a better recognition of meows emitted during isolation. Regarding the emotional valence of meows, it emerged that cat vocalizations emitted during isolation are perceived by people as the most negative, whereas those emitted during brushing are perceived as most positive. Overall, it emerged that, although meowing is mainly a human-directed vocalization and in principle represents a useful tool for cats to communicate emotional states to their owners, humans are not particularly able to extract precise information from cats’ vocalizations and show a limited capacity of discrimination based mainly on their experience with cats and influenced by empathy toward them.

Cats employ vocalizations for communicating information, thus their sounds can carry a wide range of meanings. Concerning vocalization, an aspect of increasing relevance directly connected with the welfare of such animals is its emotional interpretation and the recognition of the production context. To this end, this work presents a proof of concept facilitating the automatic analysis of cat vocalizations based on signal processing and pattern recognition techniques, aimed at demonstrating if the emission context can be identified by meowing vocalizations, even if recorded in sub-optimal conditions. We rely on a dataset including vocalizations of Maine Coon and European Shorthair breeds emitted in three different contexts: waiting for food, isolation in unfamiliar environment, and brushing. Towards capturing the emission context, we extract two sets of acoustic parameters, i.e., mel-frequency cepstral coefficients and temporal modulation features. Subsequently, these are modeled using a classification scheme based on a directed acyclic graph dividing the problem space. The experiments we conducted demonstrate the superiority of such a scheme over a series of generative and discriminative classification solutions. These results open up new perspectives for deepening our knowledge of acoustic communication between humans and cats and, in general, between humans and animals.

Obstacle avoidance is a major challenge during independent mobility for blind or visually impaired (BVI) people. Typically, BVI people can only perceive obstacles at a short distance (about 1 m, in case they are using the white cane), and some obstacles are hard to detect (e.g . , those elevated from the ground), or should not be hit by the white cane (e.g . , a standing person). A solution to these problems can be found in recent computer-vision techniques that can run on mobile and wearable devices to detect obstacles at a distance. However, in addition to detecting obstacles, it is also necessary to convey information about them in real time. This contribution presents WatchOut , a sonification technique for conveying real-time information about the main properties of an obstacle to a BVI person, who can then use this additional feedback to safely navigate in the environment. WatchOut was designed with a user-centered approach, involving four iterations of online listening tests with BVI participants in order to define, improve and evaluate the sonification technique, eventually obtaining an almost perfect recognition accuracy. WatchOut was also implemented and tested as a module of a mobile app that detects obstacles using state-of-the-art computer vision technology. Results show that the system is considered usable and can guide the users to avoid more than 85% of the obstacles.

In this article, we describe the MX-Onto ontology for providing a Semantic Web compatible representation of music resources based on their context. The context representation is realized by means of an OWL ontology that describes music information and that defines rules and classes for a flexible genre classification. By flexible classification we mean that the proposed approach enables capturing the subjective interpretation of music genres by defining multiple membership relations between a music resource and the corresponding music genres, thus supporting context-based and proximity-based search of music resources.