Evaluation of deep marginal feedback cancellation for hearing aids using speech and music.

Speech and music both play fundamental roles in daily life. Speech is important for communication while music is important for relaxation and social interaction. Both speech and music have a large dynamic range. This does not pose problems for listeners with normal hearing. However, for hearing-impaired listeners, elevated hearing thresholds may result in low-level portions of sound being inaudible. Hearing aids with frequency-dependent amplification and amplitude compression can partly compensate for this problem. However, the gain required for low-level portions of sound to compensate for the hearing loss can be larger than the maximum stable gain of a hearing aid, leading to acoustic feedback. Feedback control is used to avoid such instability, but this can lead to artifacts, especially when the gain is only just below the maximum stable gain. We previously proposed a deep-learning method called DeepMFC for controlling feedback and reducing artifacts and showed that when the sound source was speech DeepMFC performed much better than traditional approaches. However, its performance using music as the sound source was not assessed and the way in which it led to improved performance for speech was not determined. The present paper reveals how DeepMFC addresses feedback problems and evaluates DeepMFC using speech and music as sound sources with both objective and subjective measures. DeepMFC achieved good performance for both speech and music when it was trained with matched training materials. When combined with an adaptive feedback canceller it provided over 13 dB of additional stable gain for hearing-impaired listeners.

Speech quality evaluation of a sparse coding shrinkage noise reduction algorithm with normal hearing and hearing impaired listeners.

Although there are numerous papers describing single-channel noise reduction strategies to improve speech perception in a noisy environment, few studies have comprehensively evaluated the effects of noise reduction algorithms on speech quality for hearing impaired (HI). A model-based sparse coding shrinkage (SCS) algorithm has been developed, and has shown previously (Sang et al., 2014) that it is as competitive as a state-of-the-art Wiener filter approach in speech intelligibility. Here, the analysis is extended to include subjective quality ratings and a method called Interpolated Paired Comparison Rating (IPCR) is adopted to quantitatively link the benefit of speech intelligibility and speech quality. The subjective quality tests are performed through IPCR to efficiently quantify noise reduction effects on speech quality. Objective measures including frequency-weighted segmental signal-to-noise ratio (fwsegSNR), perceptual evaluation of speech quality (PESQ) and hearing aid speech quality index (HASQI) are adopted to predict the noise reduction effects. Results show little difference in speech quality between the SCS and the Wiener filter algorithm but a difference in quality rating between the HI and NH listeners. HI listeners generally gave better quality ratings of noise reduction algorithms than NH listeners. However, SCS reduced the noise more efficiently at the cost of higher distortions that were detected by NH but not by the HI. SCS is a promising candidate for noise reduction algorithms for HI. In general, care needs to be taken when adopting algorithms that were originally developed for NH participants into hearing aid applications. An algorithm that is evaluated negatively with NH might still bring benefits for HI participants.