  Categorization is crucial for content description in archiving of music
signals. On many occasions, human brain fails to classify the instruments
properly just by listening to their sounds which is evident from the human
response data collected during our experiment. Some previous attempts to
categorize several musical instruments using various linear analysis methods
required a number of parameters to be determined. In this work, we attempted to
categorize a number of string instruments according to their mode of playing
using latest-state-of-the-art robust non-linear methods. For this, 30 second
sound signals of 26 different string instruments from all over the world were
analyzed with the help of non linear multifractal analysis (MFDFA) technique.
The spectral width obtained from the MFDFA method gives an estimate of the
complexity of the signal. From the variation of spectral width, we observed
distinct clustering among the string instruments according to their mode of
playing. Also there is an indication that similarity in the structural
configuration of the instruments is playing a major role in the clustering of
their spectral width. The observations and implications are discussed in
detail.
