Traditional Machine Learning for Pitch Detection
TABLE II: Voicing decision error rate (in %) for the baselines and proposed approaches.
['RAPT', 'SWIPE’', 'DIO', 'K-means,T', 'K-means,M', 'GMM,T', 'GMM,M', 'Log. Regr.', 'KNN', 'Adaboost', 'Rand. For.', 'SVM', 'MLP']
Harvest was found to be particularly poor (VDE=23.59%), although we used the original implementation released by its authors with default settings. Across the state-of-the-art methods, RAPT and SWIPE’ provide the best results, with a VDE around 5.5%. Through our experiments, we found out that adding the MS features only slightly helped (3% relative improvement), while it turned out to be important to embed contextual frames. Indeed, stacking the previous and next frames to the current one brought a 15% relative improvement for both unsupervised and supervised approaches. Extending the context to more frames did not help.