A data-driven residual gain approach for two-stage speech enhancement
Yu Jin, Chul Min Lee, Kiho Cho, Nam Soo Kim · 2011
In this paper, we propose a novel speech enhancement algorithm based on data-driven residual gain estimation. The system consists of two stages. A noisy input signal is processed at the first stage by a conventional speech enhancement module from which both the enhanced signal and several signal to-noise ratio (SNR)-related parameters are obtained. At the second stage, the residual gain, which is estimated by a data driven method, is applied to the enhanced signal to further adjust it. According to the experimental results, the proposed algorithm has been found to show better performances compared with the conventional speech enhancement technique based on soft decision as well as the data-driven approach using the SNR grid look-up table.