  Onset detection is the process of identifying the start points of musical
note events within an audio recording. While the detection of percussive onsets
is often considered a solved problem, soft onsets-as found in string instrument
recordings-still pose a significant challenge for state-of-the-art algorithms.
The problem is further exacerbated by a paucity of data containing expert
annotations and research related to best practices for curating soft onset
annotations for string instruments. To this end, we investigate inter-annotator
agreement between 24 participants, extend an algorithm for determining the most
consistent annotator, and compare the performance of human annotators and
state-of-the-art onset detection algorithms. Experimental results reveal a
positive trend between musical experience and both inter-annotator agreement
and performance in comparison with automated systems. Additionally, onsets
produced by changes in fingering as well as those from the cello were found to
be particularly challenging for both human annotators and automatic approaches.
To promote research in best practices for annotation of soft onsets, we have
made all experimental data associated with this study publicly available. In
addition, we publish the ARME Virtuoso Strings dataset, consisting of over 144
recordings of professional performances of an excerpt from Haydn's string
quartet Op. 74 No. 1 Finale, each with corresponding individual instrumental
onset annotations.
