An efficient approach combined with harmonic and shift invariance for piano music multi-pitch detection

Abstract

We propose an efficiently discriminative method that using AdaBoost as binary classifiers combined with musical signal properties for polyphonic piano music multi-pitch detection. As features, we use spectral components of multiples and divisions of notes’ fundamental frequency, which can reduce note’s feature redundancy compared with full spectrum. For the frame-level multi-pitch detection, the features of notes have adjacent pitches are similar (we called it shift invariance), which inspires us to use one binary classifier to detect those notes’ pitch. In a certain extent, those adjacent notes improves the classifier’s generalizability. In the post-processing stage, to combine with time property, we concatenate each notes’ several continuously frame-level predictions as their new features for final pitch detection. In conclusion, the proposed method with fewer classifiers achieves better performance compared with other methods.