A Survey on Statistical Based Single Channel Speech Enhancement Techniques

Abstract

Speech enhancement is a long standing problem with various applications like hearing aids, automatic recognition and coding of speech signals. Single channel speech enhancement technique is used for enhancement of the speech degraded by additive background noises. The background noise can have an adverse impact on our ability to converse without hindrance or smoothly in very noisy environments, such as busy streets, in a car or cockpit of an airplane. Such type of noises can affect quality and intelligibility of speech. This is a survey paper and its object is to provide an overview of speech enhancement algorithms so that enhance the noisy speech signal which is corrupted by additive noise. The algorithms are mainly based on statistical based approaches. Different estimators are compared. Challenges and Opportunities of speech enhancement are also discussed. This paper helps in choosing the best statistical based technique for speech enhancement

Estimation of Noise in Nonstationary Signals Using Derivative of NLMS Algorithm

“…The adaptive filter is still better tool to suppress the background noise and estimate the original signal in noisy environment for both speech and bioelectric signals [21]. Researchers are worked on different forms of adaptive filter like least mean square (LMS), Leaky LMS, NLMS and Recursive least square (RLS) algorithm for speech enhancement [25], removal of noise in EEG and extraction of fetal ECG from maternal ECG [20].…”