Supervised sparse coding strategy in hearing aids
Hearing impaired people are struggling more understanding speech that is corrupted with noise than normal hearing listeners. In this paper we develop a supervised single channel sparse coding (SC) strategy for hearing aid (HA) users in noise environment. In this algorithm, the sparse coding and shrinkage principles are applied to noisy speech. The algorithm is implemented in the temporal domain by arranging one-dimensional speech into a data matrix. The strategy not only reduces background noise but also extracts key information from speech. The performance of the supervised sparse coding strategy is compared with other state-of-art noise reduction strategies (Wiener filtering and spectral subtraction) in both objective and subjective experiments. Results show that sparse coding leads to better sound quality (objective measures) and preserves the level of intelligibility (subjective measures).