Musical Instrument Recognition using Biologically Inspired Filtering of Temporal Dictionary Atoms

Most musical instrument recognition systems rely entirely upon spectral information instead of temporal information. In this paper, we test the hypothesis that temporal information can improve upon the accuracy achievable by the state of the art in instrument recognition. Unlike existing temporal classification methods which use traditional features such as temporal moments, we extract novel features from temporal atoms generated by nonnegative matrix factorization by using a multiresolution gamma filterbank. Among isolated sounds taken from twenty-four instrument classes, the proposed system can achieve 92.3% accuracy, thus improving upon the state of the art.