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mmsp2021-audio2scorealignment's Issues

Adjustment of initial transcription offsets, also affects pitches

self.perfm -= np.min(self.perfm[:, 1:3])

self._ground_truth -= np.min(self._ground_truth[:, 1:3])

self.score -= np.min(self.score[:, 1:3])

should be like:

 self.perfm[:, 1:3] -= np.min(self.perfm[:, 1:3]) 

Such an error could affect TAFE, EIFE, SEBA, and transcription evaluation. Specifically:

  • Since the transcription first onsets are likely the same as the ground-truth, the transcription evaluation is only partially affected, likely in a negligible way
  • Since in ASMD the first onset of the score is 0, the SEBA method is not affected at all, because it only uses the pitches of the score
  • TAFE and EIFE could may be affected by such error in that they compare the score pitches to the transcription pitches, and this error makes them not comparable; we can expect an improvement in both the two methods

As consequence, the main conclusions of the paper, should not change

  • EIFE is far better than TAFE on piano music
  • SEBA is better than TAFE and EIFE on non-piano music
  • Bytedance model works better than Omnizart model

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