Composers may not provide instructions for playing their works, especially for instrument solos, and therefore, different musicians may give very different interpretations of the same work. Such differences usually lead to time, amplitude, or frequency variations of musical notes in a phrase in the signal point of view. This paper proposes a frame-based recursive regularization method for time-dependent analysis of each note presenting in solo violin recordings. The system of equations evolves when a new frame is added and an old frame is dropped to track the varying characteristics of violin playing. This method is compared with a time-dependent non-negative matrix factorization method. The complete recordings of both BWV 1005 No. 3 played by Kuijken and 24 Caprices op. 1 no. 24 in A minor played by Paganini are used for the transcription experiment, where the proposed method performs strongly. The analysis results of a short passage extracted from BWV 1005 No. 3 performed by three famous violinists reveal numerous differences in the styles and performances of these violinists.
This study presents a discussion on the task of score alignment, which properly aligns an audio recording with its corresponding score. Conventional methods have difficulty performing this task because of asynchrony in the recording of simultaneous notes in the score. A note-based score alignment based on the pitch-by-time feature is proposed, called the piano-roll feature, and it presents an approach for converting the audio spectrogram to a piano-roll-like feature. Score-driven non-negative matrix factorisation is then adopted in the transformation. Furthermore, this study also proposes pitch-wise alignment considering each pitch sequence (i.e. the row of piano roll) separately. Results based on the MIDI-Aligned Piano Sounds database show that approximately 88% of notes match their onsets, deviating from the ground truth by less than 50 ms. Other results based on SCREAM Music Annotation Project database that is a manual annotation project of commercial CD recordings are presented as well.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.