FFT and Wavelet-Based Feature Extraction for Acoustic Audio Classification

Speech is one of the vital signals of acoustic classification. Speech recognition is also significant and very well known of audio processing. Speech contains very important frequency information of human being. The features of Audio, especially speech signal may be extracted using FFT (Fast Fourier Transform) and Wavelet to detect the frequency information of the signal. But it is difficult to extract the changes of small variation of speech signal with time-varying morphological characteristics. So, it is needed to be extracted by signal processing method because there are not visible of graphical audio signal. In this paper, an improved wavelet method has been proposed to extract the precise detection of small abnormalities of both original and noise corrupted speech signal which are taken empirically by writing MATLAB program. The proposed wavelet method found to be more summarized over conventional FFT and Wavelet in finding the small abnormalities of audio signal.