Automatic Music Transcription using row weighted decompositions

Automatic Music Transcription (AMT) seeks to understand a musical piece in terms of note activities. Matrix decomposition methods are often used for AMT, seeking to decompose a spectrogram over a dictionary matrix of note-specific template vectors. The performance of these methods can suffer due to the large harmonic overlap found in tonal musical spectra. We propose a row weighting scheme that transforms each spectrogram frame and the dictionary, with the weighting determined by the effective correlations in the decomposition. Experiments show improved AMT performance.