Ryan M. Corey

Kanad Sarkar, Austin Lu, Manan Mittal et al.

Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS.

Ryan M. Corey, Uriah Jones, Andrew C. Singer

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.

Ryan M. Corey, Andrew C. Singer

Augmented listening devices, such as hearing aids and augmented reality headsets, enhance human perception by changing the sounds that we hear. Microphone arrays can improve the performance of listening systems in noisy environments, but most array-based listening systems are designed to isolate a single sound source from a mixture. This work considers a source-remixing filter that alters the relative level of each source independently. Remixing rather than separating sounds can help to improve perceptual transparency: it causes less distortion to the signal spectrum and especially to the interaural cues that humans use to localize sounds in space.

Ryan M. Corey, Andrew C. Singer

Microphone arrays are usually assumed to have rigid geometries: the microphones may move with respect to the sound field but remain fixed relative to each other. However, many useful arrays, such as those in wearable devices, have sensors that can move relative to each other. We compare two approaches to beamforming with deformable microphone arrays: first, by explicitly tracking the geometry of the array as it changes over time, and second, by designing a time-invariant beamformer based on the second-order statistics of the moving array. The time-invariant approach is shown to be appropriate when the motion of the array is small relative to the acoustic wavelengths of interest. The performance of the proposed beamforming system is demonstrated using a wearable microphone array on a moving human listener in a cocktail-party scenario.

Ryan M. Corey, Matthew D. Skarha, Andrew C. Singer

Augmented listening devices such as hearing aids often perform poorly in noisy and reverberant environments with many competing sound sources. Large distributed microphone arrays can improve performance, but data from remote microphones often cannot be used for delay-constrained real-time processing. We present a cooperative audio source separation and enhancement system that leverages wearable listening devices and other microphone arrays spread around a room. The full distributed array is used to separate sound sources and estimate their statistics. Each listening device uses these statistics to design real-time binaural audio enhancement filters using its own local microphones. The system is demonstrated experimentally using 10 speech sources and 160 microphones in a large, reverberant room.

Ryan M. Corey, Naoki Tsuda, Andrew C. Singer

We present an open-access dataset of over 8000 acoustic impulse from 160 microphones spread across the body and affixed to wearable accessories. The data can be used to evaluate audio capture and array processing systems using wearable devices such as hearing aids, headphones, eyeglasses, jewelry, and clothing. We analyze the acoustic transfer functions of different parts of the body, measure the effects of clothing worn over microphones, compare measurements from a live human subject to those from a mannequin, and simulate the noise-reduction performance of several beamformers. The results suggest that arrays of microphones spread across the body are more effective than those confined to a single device.