Investigation on the Effect of the Input Features in the Noise Level Classification of Noisy Speech

Abstract

Noise Level Estimation plays a crucial role in Speech Enhancement (SE) Algorithms. Recently, few noise estimation (NE) algorithms are developed for SE using the minimal-tracking method, but there is little research done in the noise level classification (NLC). Therefore, there is a need to identify appropriate audio features that are required for the NLC. In this paper, this problem has been addressed and seventeen audio features of the noisy speech are examined for NLC using four different types of standard and efficient classifiers such as K-Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) classifiers. The features are first optimized to achieve the best classification performance using the Principal Component Analysis (PCA) and the Neighbourhood Component Feature Selection (NCFS) method. Finally, a comparative performance analysis is carried out by taking six different categories of real-life noisy speech signals from the standard speech database and then the best set of features are reported and the best performing classifier for the NLC is identified.