Full-duplex acoustic interaction system for cognitive experiments with cetaceans

Cetaceans show high cognitive abilities and strong social bonds. Acoustics is their primary modality to communicate and sense the environment. Research on their echolocation and vocalizations with conspecifics and with humans typically uses visual and tactile systems adapted from research on primates or birds. Such research would benefit from a purely acoustic communication system in which signals flow in both directions simultaneously. We designed and implemented a full duplex system to acoustically interact with cetaceans in the wild, featuring digital echo-suppression. We pilot tested the system in Arctic Norway and achieved an echo suppression of 18 dB leaving room for technical improvements addressed in the discussion. Nevertheless, the system enabled vocal interaction with the underwater acoustic scene by allowing experimenters to listen while producing sounds. We describe our motivations, then present our pilot deployment and give examples of initial explorative attempts to vocally interact with wild orcas and humpback whales

Character-Level Translation with Self-attention

We explore the suitability of self-attention models for character-level neural machine translation. We test the standard transformer model, as well as a novel variant in which the encoder block combines information from nearby characters using convolutions. We perform extensive experiments on WMT and UN datasets, testing both bilingual and multilingual translation to English using up to three input languages (French, Spanish, and Chinese). Our transformer variant consistently outperforms the standard transformer at the character-level and converges faster while learning more robust character-level alignments