1 research outputs found
Music and Vocal Separation Using Multi-Band Modulation Based Features
The potential use of non-linear speech features has not been investigated for
music analysis although other commonly used speech features like Mel Frequency
Ceptral Coefficients (MFCC) and pitch have been used extensively. In this
paper, we assume an audio signal to be a sum of modulated sinusoidal and then
use the energy separation algorithm to decompose the audio into amplitude and
frequency modulation components using the non-linear Teager-Kaiser energy
operator. We first identify the distribution of these non-linear features for
music only and voice only segments in the audio signal in different Mel spaced
frequency bands and show that they have the ability to discriminate. The
proposed method based on Kullback-Leibler divergence measure is evaluated using
a set of Indian classical songs from three different artists. Experimental
results show that the discrimination ability is evident in certain low and mid
frequency bands (200 - 1500 Hz).Comment: 5 pages, 5 figures, 2010 IEEE Symposium on Industrial Electronics
Applications (ISIEA