An intelligent sparse feature extraction approach for music data component recognition and analysis of hybrid instruments
Yi Liao, Zhen Gui
Abstract
In this paper, a sparse feature extraction method is presented based on sparse decomposition and multiple musical instrument component dictionaries to address the challenges of existing methods in component-recognition and analysis of mixed musical instrument music data. These methods, which are often dependent on data labels, and rely primarily on frequency domain or physical features, can be improved significantly using this technique. Through the in-depth analysis of the sparse coefficient vectors, this method is capable of generating independent sparse music features that are highly interpretable and have been shown to intuitively express the composition of musical instruments, and capture the variations of emotion in the music. Consequently, this approach has great potential for application in the field of mixed musical instrument composition analysis and other time-varying signal analysis.