Music and Vocal Separation Using Multi-Band Modulation Based Features
This work addresses music analysis for audio processing applications, but it is incremental as it applies existing non-linear speech features to a new domain with limited data.
The paper tackled the problem of separating music and vocals in audio signals by proposing a method using multi-band modulation features derived from the Teager-Kaiser energy operator, and found that these features effectively discriminate between music and voice in low to mid frequency bands (200-1500 Hz) in Indian classical songs.
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).