Ryan M. Corey

The purpose of this study was to compare the effects of source-specific (independent) and conventional dynamic range compression (DRC) on sound quality ratings among listeners with hearing loss when ground-truth signals are available to the compressor. Twenty listeners with mild to moderately severe sensorineural hearing loss rated the sound quality on different subscales for two types of signal mixtures: speech in music (Overall Sound Quality, Speech Clarity, and Music Pleasantness) and speech in noise (Overall Sound Quality, Speech Clarity, and Noisiness) at three speech-to-background ratios (SBR: -10, 0, +10 dB). Speech in music was a 10-second-long spontaneous speech excerpt with a duration-matched classical music excerpt. Speech in noise had the same speech signal mixed with speech-shaped noise. Conventional DRC applied a 50-ms release time to the mixed signals, whereas independent DRC applied a 50-ms release time for speech and a 2000-ms release time for music or noise before mixing. The control conditions included linear amplification applied to the signals before and after mixing. Independent DRC resulted in higher overall sound quality, music pleasantness, and lower noisiness ratings than conventional DRC, regardless of SBR. Independent DRC resulted in higher speech clarity ratings than conventional DRC at lower SBRs. The results are generally supported by acoustic metrics, indicating more effective compression and higher output SBRs, especially at lower input SBRs, and reduced across-source modulation correlations with independent DRC. These findings extend the literature on the advantages of independent compression of sound sources by demonstrating sound quality benefits across multiple sub-scales for listeners with hearing loss across music and noise backgrounds.

In listening devices, such as hearing aids and augmented-reality headsets, it may be desirable to apply separate processing to the user’s own speech and to external sounds. In particular, listeners are most sensitive to processing delay for their own speech. If own-speech signals were processed along a dedicated low-delay pathway, then complex external sounds could be processed with more relaxed delay constraints. This approach is especially relevant to high-latency digital wireless systems, such as remote microphones and distributed sensor networks. The proposed mixed-delay beamforming system isolates the user’s own speech using a two-path structure resembling a sidelobe canceller. A fixed, causal beamformer is applied to the local microphones of the listening device. A second adaptive beamformer uses inputs from both local and remote microphones to separate external sounds. The proposed system is demonstrated using recordings of a group conversation with stationary and moving subjects.

Wireless assistive listening devices (ALDs), such as induction loops, radio-frequency transmitters, and digital streaming systems, improve accessibility for people with hearing loss by transmitting from a venue’s sound system directly to the listener. Today, ALDs are used primarily for lectures and performances. When paired with advanced hearing devices, however, they could form part of an augmented listening system that lets users "remix" sounds in their environment, including from loudspeakers in public spaces. For example, users could amplify public announcements or suppress background music while having a conversation. In the proposed system, a binaural adaptive filter uses the ALD signal to estimate the loudspeaker sound at the ears. The hearing device can then either enhance or remove the loudspeaker sound in the hearing device output while preserving other nearby sounds. We demonstrate the proposed system using several commercial ALDs and assess the effects of delay, bandwidth, distortion, and noise on real-world system performance.

Hearing aids use dynamic range compression (DRC), a form of automatic gain control, to make quiet sounds louder and loud sounds quieter. Compression can improve listening comfort, but it can also cause unwanted distortion in noisy environments. It has been widely reported that DRC performs poorly in noise, but there has been little mathematical analysis of these noise-induced distortion effects. This work introduces a mathematical model to study the behavior of DRC in noise. By making simplifying assumptions about the signal envelopes, we define an effective compression function that models the compression applied to one signal in the presence of another. Using the properties of concave functions, we prove results about DRC that have been previously observed experimentally: that the effective compression applied to each sound in a mixture is weaker than it would have been for the signal alone; that uncorrelated signal envelopes become negatively correlated when compressed as a mixture; and that compression can reduce the long-term signal-to-noise ratio in certain conditions. These theoretical results are supported by software experiments using recorded speech signals.

The COVID-19 pandemic disrupted the world in 2020 by spreading at unprecedented rates and causing tens of thousands of fatalities within a few months. The number of deaths dramatically increased in regions where the number of patients in need of hospital care exceeded the availability of care. Many COVID-19 patients experience Acute Respiratory Distress Syndrome (ARDS), a condition that can be treated with mechanical ventilation. In response to the need for mechanical ventilators, designed and tested an emergency ventilator (EV) that can control a patient’s peak inspiratory pressure (PIP) and breathing rate, while keeping a positive end expiratory pressure (PEEP). This article describes the rapid design, prototyping, and testing of the EV. The development process was enabled by rapid design iterations using additive manufacturing (AM). In the initial design phase, iterations between design, AM, and testing enabled a working prototype within one week. The designs of the 16 different components of the ventilator were locked by additively manufacturing and testing a total of 283 parts having parametrically varied dimensions. In the second stage, AM was used to produce 75 functional prototypes to support engineering evaluation and animal testing. The devices were tested over more than two million cycles. We also developed an electronic monitoring system and with automatic alarm to provide for safe operation, along with training materials and user guides. The final designs are available online under a free license. The designs have been transferred to more than 70 organizations in 15 countries. This project demonstrates the potential for ultra-fast product design, engineering, and testing of medical devices needed for COVID-19 emergency response.

Face masks muffle speech and make communication more difficult, especially for people with hearing loss. This study examines the acoustic attenuation caused by different face masks, including medical, cloth, and transparent masks, using a head-shaped loudspeaker and a live human talker. The results suggest that all masks attenuate frequencies above 1 kHz, that attenuation is greatest in front of the talker, and that there is substantial variation between mask types, especially cloth masks with different materials and weaves. Transparent masks have poor acoustic performance compared to both medical and cloth masks. Most masks have little effect on lapel microphones, suggesting that existing sound reinforcement and assistive listening systems may be effective for verbal communication with masks.

In response to anticipated shortages of ventilators caused by the COVID-19 pandemic, many organizations have designed low-cost emergency ventilators. Many of these devices are pressure-cycled pneumatic ventilators, which are easy to produce but often do not include the sensing or alarm features found on commercial ventilators. This work reports a low-cost, easy-to-produce electronic sensor and alarm system for pressure-cycled ventilators that estimates clinically useful metrics such as pressure and respiratory rate and sounds an alarm when the ventilator malfunctions. A low-complexity signal processing algorithm uses a pair of nonlinear recursive envelope trackers to monitor the signal from an electronic pressure sensor connected to the patient airway. The algorithm, inspired by those used in hearing aids, requires little memory and performs only a few calculations on each sample so that it can run on nearly any microcontroller.