Scene recognition algorithm based on multi-feature and weighted minimum distance classifier for digital hearing aids

摘要

The recognition precision of the existing auditory scene recognition algorithms is relatively satisfactory, but they can only be applied to several noise scenarios, and it can't meet the performance requirements of digital hearing aids in complex environment. In order to solve the above problems, scene recognition algorithm based on multi-feature and weighted minimum distance classifier is proposed in this paper. In this algorithm, the speech endpoint detection algorithm based on the band-partitioning spectral entropy and spectral energy is used to divide the noisy speech into speech segment and noise segment. Then the characteristics such as Critical Band Ratio and band-partitioning spectral entropy as well as adaptive short-time zero crossing rate of each segment are extracted for the weighted minimum distance classifier to recognize the noise scenario. The experiments result shows that the proposed algorithm has strong robustness and high accuracy. It's suitable to be applied in digital hearing aids.