Instrument identification in solo and ensemble music using Independent Subspace Analysis

We investigate the use of Independent Subspace Analysis (ISA) for instrument identification in musical recordings. We represent short-term log-power spectra of possibly polyphonic music as weighted non-linear combinations of typical note spectra plus background noise. These typical note spectra are learnt either on databases containing isolated notes or on solo recordings from different instruments. We show that this model has some theoretical advantages over methods based on Gaussian Mixture Models (GMM) or on linear ISA. Preliminary experiments with five instruments and test excerpts taken from commercial CDs give promising results. The performance on clean solo excerpts is comparable with existing methods and shows limited degradation under reverberant conditions. Applied to a difficult duo excerpt, the model is also able to identify the right pair of instruments and to provide an approximate transcription of the notes played by each instrument.