Sparse cepstral codes and power scale for instrument identification

This paper presents a novel feature representation called sparse cepstral codes for instrument identification. We first motivate the approach by discussing why cepstrum is suitable for instrument identification. Then we propose the use of sparse coding and power normalization to derive compact codes that better represent the information of the cepstrum. Our evaluation on both uni-source and multi-source instrument identification tasks show that the proposed feature leads to significantly better accuracy than existing methods. We further show that cepstrum obtained from power-scaled spectrum can do better than typical cepstrum especially in multi-source signal. The proposed system achieves 0.955 F-score in uni-source dataset and 0.688 F-score in multi-source dataset.