Computational Methods for the Alignment and Score-Informed Transcription of Piano Music
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This thesis is concerned with computational methods for alignment and score-informed
transcription of piano music. Firstly, several methods are proposed to improve the alignment
robustness and accuracywhen various versions of one piece of music showcomplex
differences with respect to acoustic conditions or musical interpretation. Secondly, score
to performance alignment is applied to enable score-informed transcription.
Although music alignment methods have considerably improved in accuracy in recent
years, the task remains challenging. The research in this thesis aims to improve the
robustness for some cases where there are substantial differences between versions and
state-of-the-art methods may fail in identifying a correct alignment. This thesis first exploits
the availability of multiple versions of the piece to be aligned. By processing these
jointly, the alignment process can be stabilised by exploiting additional examples of how
a section might be interpreted or which acoustic conditions may arise. Two methods are
proposed, progressive alignment and profile HMM, both adapted from the multiple biological
sequence alignment task. Experiments demonstrate that these methods can indeed
improve the alignment accuracy and robustness over comparable pairwise methods.
Secondly, this thesis presents a score to performance alignment method that can improve
the robustness in cases where some musical voices, such as the melody, are played asynchronously
to others – a stylistic device used in musical expression. The asynchronies between
the melody and the accompaniment are handled by treating the voices as separate
timelines in a multi-dimensional variant of dynamic time warping (DTW). The method
measurably improves the alignment accuracy for pieces with asynchronous voices and
preserves the accuracy otherwise.
Once an accurate alignment between a score and an audio recording is available, the
score information can be exploited as prior knowledge in automatic music transcription
(AMT), for scenarios where score is available, such as music tutoring. Score-informed dictionary
learning is used to learn the spectral pattern of each pitch that describes the energy
distribution of the associated notes in the recording. More precisely, the dictionary learning
process in non-negative matrix factorization (NMF) is constrained using the aligned
score. This way, by adapting the dictionary to a given recording, the proposed method
improves the accuracy over the state-of-the-art.
Authors
Wang, SiyingCollections
- Theses [4125]
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