Automatically identifying sections of solo voices or instruments within a large corpus of music recordings would be useful e.g. to construct a library of isolated instruments to train signal models. We consider several ways to identify these sections, including a baseline classifier trained on conventional speech features. Our best results, achieving frame level precision and recall of around 70%, come from an approach that attempts to track the local periodicity of an assumed solo musical voice, then classifies the segment as a genuine solo or not on the basis of what proportion of the energy can be canceled by a comb filter constructed to remove just that periodicity. This optimal cancelation filter has other applications in pitch tracking and separating periodic and aperiodic energy.

We describe an algorithm to accurately estimate the fundamental frequency of harmonic sinusoids in a mixed voice recording environment using an aligned electronic score as a guide. Taking the pitch tracking results on individual voices prior to mixing as ground truth, we are able estimate the pitch of individual voices in a 4-part piece to within 50 cents of the correct pitch more than 90% of the time.

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