Most speech processing models begin with feature extraction and then pass the vector to primary model. The solution's performance mainly depends on quality of representation model architecture. Much research focuses designing robust deep network architecture ignoring representation's important role during neural era. This work aims exploit a new approach design signal in time-frequency domain v...

This paper presents a qualitative automatic feature selection framework. Feature plays very important role in selecting those features which provides the best results terms of accuracy. The research work is aimed for depth analysis non-linear parameters using EEG signals. also comprehensive study and their interpretations characterizing epileptic seizures. We examine quality each independently ...

An Orthogonal Least Squares (OLS) based feature selection method is proposed for both binomial and multinomial classification. The novel Squared Correlation Coefficient (SOCC) defined on Error Reduction Ratio (ERR) in OLS used as the ranking criterion. equivalence between canonical correlation coefficient, Fisher’s criterion, sum of SOCCs revealed, which unveils statistical implication ERR firs...