Recorded music offers a wealth of information for studying performance practice. This paper examines the challenges of automatically extracting performance information from audio recordings of the singing voice and discusses our technique for automatically extracting information such as note timings, intonation, mbrato rates, and dynamics. An experiment is also presented that focuses on the tuning of semitones in solo soprano performances of Schubert's "Ave Maria" by non-professional and professional singers. We found a small decrease in size of intervals with a leading tone function only in the non-professional group.This paper describes the challenges that arise when attempting to automatically extract pitchrelated performance data from recordings of the singing voice. The first section of the paper provides an overview of the history of analyzing recorded performances. The second section describes an algorithm for automatically extracting performance data from recordings of the singing voice where a score of the performance is available. The algorithm first identifies note onsets and offsets. Once the onsets and offsets have been determined, intonation, vibrato, and dynamic characteristics can be calculated for each note.The main experiment of the paper, described in the third section, is a study of intonation in solo vocal performance, where both the note onsets and offsets and fundamental frequency were estimated automatically. In the study, six non-professional and six professional sopranos performed Schubert's "Ave Maria" three times a cappella and three times with a recorded piano accompaniment. Our analysis of these recordings focused on the intonation of ascending and descending semitones. We found that the A-B^ intervals with a leading tone function were on average 8 cents smaller than the non-leading tone A-Bl,, but that their average size was approximately the same as the other semitones performed in the piece, regardless of intervallic direction or accompaniment. PREVIOUS WORK ON THE ANALYSIS OF RECORDED PERFORMANCESInterest in studying recorded performances dates back almost as far as the birth of recordable media, beginning with Dayton Miller's (1916) work on visualized pitch information in recordings with phonophotographic apparati. The psychologist Carl Seashore and colleagues at the University of Iowa also undertook extensive work in performance
This paper presents a new method of encoding performance data in MEI using the recently added
This paper describes a technique to improve the accuracy of dynamic time warping-based MIDI-audio alignment. The technique implements a hidden Markov model that uses aperiodicity and power estimates from the signal as observations and the results of a dynamic time warping alignment as a prior. In addition to improving the overall alignment, this technique also identifies the transient and steady-state sections of the note. This information is important for describing various aspects of a musical performance, including both pitch and rhythmic aspects.
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