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Soft Dynamic Time Warping for Multi-Pitch Estimation and Beyond
Many tasks in music information retrieval (MIR) involve weakly aligned data, where exact temporal correspondences are unknown.
Deep-Learning Architectures for Multi-Pitch Estimation: Towards Reliable Evaluation
We therefore investigate the influence of dataset splits in the presence of several movements of a work cycle (cross-version evaluation) and propose a best-practice splitting strategy for MusicNet, which weakens the influence of individual test tracks and suppresses overfitting to specific works and recording conditions.
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