#Violin pizzicato verification

In the transcription of a specific piano, the proposed system achieves 82% on note-wise F-measure, which outperforms the state-of-the-art.

#Violin pizzicato verification

We also observe that note verification further improves the performance of transcription. Experiments show that CNN outperforms other types of neural networks in both onset detection and pitch estimation, and the combination of two CNNs yields better performance than a single CNN in note recognition. In this way, not only the pitches are picked up by note activations, but the onsets are revised. Then, the spectrogram of the segment around candidate onset is factorized using attack templates of candidate pitches. During the note verification, templates for the specific piano are generated to model the attack of note per pitch. Hence, candidate pitches at candidate onsets are obtained in the first stage. The note recognition stage is independent of piano individual, in which one CNN is used to detect onsets and another is used to estimate the probabilities of pitches at each detected onset. In the first stage, two convolutional neural networks (CNNs) are adopted to recognize the notes of the piano preliminarily, and note verification for the specific individual is conducted in the second stage.

This paper presents a two-stage transcription framework for a specific piano, which combines deep learning and spectrogram factorization techniques.