Audio-to-score Alignment at the Note Level for Orchestral Recordings

In this paper we propose an offline method for refining audio-to-score alignment at the note level in the context of orchestral recordings. State-of-the-art score alignment systems estimate note onsets with a low time resolution, and without detecting note offsets. For applications such as score-informed source separation we need a precise alignment at note level. Thus, we propose a novel method that refines alignment by determining the note onsets and offsets in complex orchestral mixtures by combining audio and image processing techniques. First, we introduce a note-wise pitch salience function that weighs the harmonic contribution according to the notes present in the score. Second, we perform image binarization and blob detection based on connectivity rules. Then, we pick the best combination of blobs, using dynamic programming. We finally obtain onset and offset times from the boundaries of the most salient blob. We evaluate our method on a dataset of Bach chorales, showing that the proposed approach can accurately estimate note onsets and offsets.