Although traditional algorithms can led to suppressed voice in the noise, but the distortion of the voice is inevitable. An introduction is made as to the speech signal enhancement with an improved threshold method. Compared MATLAB experimental simulation on simulated platform with traditional enhanced algorithm, this paper aims to verify this method can effectively remove the noise in the signal, enhanced voice quality, improve speech intelligibility, and achieve the effect of the enhanced speech signal.
In all authentication systems, biometric samples are regarded to be the most reliable one. Biometric samples like fingerprint, retina etc. is unique. Most commonly available biometric system prefers these samples as reliable inputs. In a biometric authentication system, the design of decision support system is critical and it determines success or failure. Here, we propose such a system based on neuro and fuzzy system. Neuro systems formulated using Artificial Neural Network learn from numeric data while fuzzy based approaches can track finite variations in the environment. Thus NFS systems formed using ANN and fuzzy system demonstrate adaptive, numeric and qualitative processing based learning. These attributes have motivated the formulation of an adaptive neuro fuzzy inference system which is used as a DSS of a biometric authenticable system. The experimental results show that the system is reliable and can be considered to be a part of an actual design.
Identification, Secrecy, Template, and Privacy-Leakage of Biometric Identification System under Noisy Enrollment