An efficient recurrent Rats function network (Rrfn) based speech enhancement through noise reduction

In modern communication system, speech communication is almost utilized in vast range of applications. Usually, during transmission of speech signal, environment interference causes degradation of signal. Few speech interference which affects quality of speech signal are acoustic noise, acoustic reverberation or white noise. In this research work, it is aimed to estimate the noise in the speech signal using Recurrent Function Network (RFN). The proposed technique is termed as Recurrent RATS Function Network (RRFN). The proposed network estimates the different noise exists in the input noisy speech signal. Once the noises are identified in speech signal, features are estimated using novel radial based RATS (Robust Automatic Transcription of Speech) approach. Further to enhance the clarity of speech signal, a novel generalized recursive singular value technique integrated in elliptic filter is used to effectively remove noises in the speech signal. Simulation analysis is performed for proposed RFN and compared with existing techniques in terms of PESQ and STOI. The proposed method exhibits good performance improvement over the existing techniques for different SNR levels.