Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users

The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech understanding in noise, improve by 1.6 dB SRT. Ours is the first denoising system that is implemented on a mobile device, streamed directly to users' hearing aids using only a single channel as audio input while improving user satisfaction on all tested aspects, including speech intelligibility. This includes overall preference of the denoised and streamed signal over the hearing aid, thereby accepting the higher latency for the significant improvement in speech understanding

Current in coherent quantum systems connected to mesoscopic Fermi reservoirs

We study particle current in a recently proposed model for coherent quantum transport. In this model, a system connected to mesoscopic Fermi reservoirs (meso-reservoir) is driven out of equilibrium by the action of super-reservoirs thermalized to prescribed temperatures and chemical potentials by a simple dissipative mechanism described by the Lindblad equation. We compare exact (numerical) results with theoretical expectations based on the Landauer formula

Morphological docking of secretory vesicles

Calcium-dependent secretion of neurotransmitters and hormones is essential for brain function and neuroendocrine-signaling. Prior to exocytosis, neurotransmitter-containing vesicles dock to the target membrane. In electron micrographs of neurons and neuroendocrine cells, like chromaffin cells many synaptic vesicles (SVs) and large dense-core vesicles (LDCVs) are docked. For many years the molecular identity of the morphologically docked state was unknown. Recently, we resolved the minimal docking machinery in adrenal medullary chromaffin cells using embryonic mouse model systems together with electron-microscopic analyses and also found that docking is controlled by the sub-membrane filamentous (F-)actin. Currently it is unclear if the same docking machinery operates in synapses. Here, I will review our docking assay that led to the identification of the LDCV docking machinery in chromaffin cells and also discuss whether identical docking proteins are required for SV docking in synapses