Musical instruments signal analysis and recognition using fractal features

Analyzing the structure of music signals at multiple time scales is of importance both for modeling music signals and their automatic computer-based recognition. In this paper we propose the multi-scale fractal dimension profile as a descriptor useful to quantify the multiscale complexity of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments. We compare the descriptiveness of our features against that of Mel frequency cepstral coefficients (MFCCs) using both static and dynamic classifiers, such as Gaussian mixture models (GMMs) and hidden Markov models (HMMs). The methods and features proposed in this paper are promising for music signal analysis and of direct applicability in large-scale music classification tasks.