The optimal threshold for removing noise from speech is similar across normal and impaired hearing-a time-frequency masking study.

Hearing-impaired listeners' intolerance to background noise during speech perception is well known. The current study employed speech materials free of ceiling effects to reveal the optimal trade-off between rejecting noise and retaining speech during time-frequency masking. This relative criterion value (-7 dB) was found to hold across noise types that differ in acoustic spectro-temporal complexity. It was also found that listeners with hearing impairment and those with normal hearing performed optimally at this same value, suggesting no true noise intolerance once time-frequency units containing speech are extracted.

An ideal quantized mask to increase intelligibility and quality of speech in noise.

Time-frequency (T-F) masks represent powerful tools to increase the intelligibility of speech in background noise. Translational relevance is provided by their accurate estimation based only on the signal-plus-noise mixture, using deep learning or other machine-learning techniques. In the current study, a technique is designed to capture the benefits of existing techniques. In the ideal quantized mask (IQM), speech and noise are partitioned into T-F units, and each unit receives one of N attenuations according to its signal-to-noise ratio. It was found that as few as four to eight attenuation steps (IQM4, IQM8) improved intelligibility over the ideal binary mask (IBM, having two attenuation steps), and equaled the intelligibility resulting from the ideal ratio mask (IRM, having a theoretically infinite number of steps). Sound-quality ratings and rankings of noisy speech processed by the IQM4 and IQM8 were also superior to that processed by the IBM and equaled or exceeded that processed by the IRM. It is concluded that the intelligibility and sound-quality advantages of infinite attenuation resolution can be captured by an IQM having only a very small number of steps. Further, the classification-based nature of the IQM might provide algorithmic advantages over the regression-based IRM during machine estimation.

An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker.

Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This listening situation represents a very different problem for both the human and machine listener, when compared to perceiving speech in other types of background noise. A machine learning algorithm is introduced here to address this listening situation. A deep neural network was trained to estimate the ideal ratio mask for a male target talker in the presence of a female competing talker. The monaural algorithm was found to produce sentence-intelligibility increases for hearing-impaired (HI) and normal-hearing (NH) listeners at various signal-to-noise ratios (SNRs). This benefit was largest for the HI listeners and averaged 59%-points at the least-favorable SNR, with a maximum of 87%-points. The mean intelligibility achieved by the HI listeners using the algorithm was equivalent to that of young NH listeners without processing, under conditions of identical interference. Possible reasons for the limited ability of HI listeners to perceptually segregate concurrent voices are reviewed as are possible implementation considerations for algorithms like the current one.

Increasing Robustness of Brain-Computer Interfaces Through Automatic Detection and Removal of Corrupted Input Signals.

For brain-computer interfaces (BCIs) to be viable for long-term daily usage, they must be able to quickly identify and adapt to signal disruptions. Furthermore, the detection and mitigation steps need to occur automatically and without the need for user intervention while also being computationally tractable for the low-power hardware that will be used in a deployed BCI system. Here, we focus on disruptions that are likely to occur during chronic use that cause some recording channels to fail but leave the remaining channels unaffected. In these cases, the algorithm that translates recorded neural activity into actions, the neural decoder, should seamlessly identify and adjust to the altered neural signals with minimal inconvenience to the user. First, we introduce an adapted statistical process control (SPC) method that automatically identifies disrupted channels so that both decoding algorithms can be adjusted, and technicians can be alerted. Next, after identifying corrupted channels, we demonstrate the automated and rapid removal of channels from a neural network decoder using a masking approach that does not change the decoding architecture, making it amenable for transfer learning. Finally, using transfer and unsupervised learning techniques, we update the model weights to adjust for the corrupted channels without requiring the user to collect additional calibration data. We demonstrate with both real and simulated neural data that our approach can maintain high-performance while simultaneously minimizing computation time and data storage requirements. This framework is invisible to the user but can dramatically increase BCI robustness and usability.