Cochlear Limited

About: Cochlear Limited is a company organization based out in Sydney, New South Wales, Australia. It is known for research contribution in the topics: Cochlear implant & Hearing loss. The organization has 1290 authors who have published 1479 publications receiving 33109 citations. The organization is also known as: кохлеарные Americas & COCHLEAR LIMITED.

Alpha and Theta EEG Variations as Indices of Listening Effort to Be Implemented in Neurofeedback Among Cochlear Implant Users

Abstract: Theta and Alpha EEG rhythms appear useful for the listening effort estimation. In particular, Alpha would inhibit irrelevant stimuli, and Theta underlies working memory and processing. The balance between them seems essential for the word recognition, therefore evaluating the listening effort experienced by hearing impaired patients appears worthy, since noise/distortions in a speech signal increase listening effort. Aim of the study was the estimation of the effort during word in noise recognition under different noise conditions, and along the task. Results showed an increase in the frontal Theta and parietal Alpha for a difficult (but not the most difficult) noise condition, and for Theta in correspondence of the stimulus. Additionally, frontal Theta activity increased along the task for the same difficult noise condition during and after the stimulus.

Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning

Abstract: Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset are available at \url{this https URL}.

Electrode array and method of forming an electrode array

Abstract: A method of forming an electrode array is disclosed, the method comprising forming an elongate comb structure comprising a plurality of longitudinally-spaced electrode contacts extending from and supported by a spine, electrically connecting each of a plurality of electrically conductive pathways to a respective one of the plurality of electrode contacts, placing the conductive pathways adjacent the contacts, placing silicone over the conductive pathways and contacts, curing the silicone so as to substantially retain the longitudinal spacing between neighboring contacts, and severing the spine from the plurality of electrode contacts.

Neural Audio Fingerprint for High-Specific Audio Retrieval Based on Contrastive Learning

Abstract: Most of existing audio fingerprinting systems have limitations to be used for high-specific audio retrieval at scale. In this work, we generate a low-dimensional representation from a short unit segment of audio, and couple this fingerprint with a fast maximum inner-product search. To this end, we present a contrastive learning framework that derives from the segment-level search objective. Each update in training uses a batch consisting of a set of pseudo labels, randomly selected original samples, and their augmented replicas. These replicas can simulate the degrading effects on original audio signals by applying small time offsets and various types of distortions, such as background noise and room/microphone impulse responses. In the segment-level search task, where the conventional audio fingerprinting systems used to fail, our system using 10x smaller storage has shown promising results. Our code and dataset are available at https://mimbres.github.io/neural-audio-fp/.