Music is a mysterious language that conveys feeling and thoughts via
different tones and timbre. For better understanding of timbre in music, we
chose music data of 6 representative instruments, analysed their timbre
features and classified them. Instead of the current trend of Neural Network
for black-box classification, our project is based on a combination of MFCC and
LPC, and augmented with a 6-dimensional feature vector designed by ourselves
from observation and attempts. In our white-box model, we observed significant
patterns of sound that distinguish different timbres, and discovered some
connection between objective data and subjective senses. With a totally
32-dimensional feature vector and a naive all-pairs SVM, we achieved improved
classification accuracy compared to a single tool. We also attempted to analyze
music pieces downloaded from the Internet, found out different performance on
different instruments, explored the reasons and suggested possible ways to
improve the performance