TAPE: An End-to-End Timbre-Aware Pitch Estimator - Tamer, Nazif Can; Özer, Yigitcan; Müller, Meinard; Serra, Xavier (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2023)
Pitch estimation of a target musical source within a multi-source polyphonic signal is of great interest for music performance analysis. One possible approach for extracting the pitch of a target source is to first perform source separation and then estimate the pitch of the separated track. However, as we will show, this typically leads to poor results. As an alternative to this approach, we introduce a timbre-aware pitch estimator (TAPE), which estimates the pitch of a target source in an end-to-end manner without the need for an explicit source separation step. Opposed to existing approaches that assume the predominance of a lead voice, our approach builds upon other cues that only rely on the timbral characteristics. Our results on real violin-piano duets show that, without any pre-processing step, TAPE trained on synthetic mixes outperforms the sequential procedure of source separation and pitch estimation under many settings, even if the target source is not predominant.
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Keywords: Audio Source Separation; Music Performance Analysis; Pitch Estimation; Weakly Supervised Learning