Investigation of Window Effects and the Accurate Estimation of Spectral Centroid
The spectral centroid is one of the useful low level features of a signal that was proposed for speech-music classification, speech recognition and musical instrument classification, and was also considered one of the lowlevel features to describe the audio content in MPEG-7 Content Description and Interface Standard. When the spectral centroid is computed from practical data, the estimate is different from the true expected theoretical value. Moreover, the behavior of the estimation error, when computed from finite length data i.e. from a short segment of signal would of high interest because most of the classification algorithms use dynamic features as the signals are nonstationary. In this paper, windowing effects on the spectral centroid estimation are investigated considering some well structured signals that appear frequently in speech and audio content. A novel algorithm is proposed to counter the window effects and better estimation of spectral centroid.
