Annotation of Soft Onsets in String Ensemble Recordings

Supplementary experimental results for the paper Annotation of Soft Onsets in String Ensemble Recordings.

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 also publish the ARME Virtuoso Strings Dataset, consisting of over 144 recordings of professional performances of an excerpt from Haydn's Op. 74 No. 1 Finale, each with corresponding individual instrumental note onset annotations.

If you use the resources on this website in your research, feel free to cite this paper:

            
@article{tomczak2022annotation,
  title={Annotation of Soft Onsets in String Ensemble Recordings}, 
  author={Tomczak, Maciej and Li, Min Susan and Bradbury, Adrian and Elliott, Mark and Stables, Ryan and Witek, Maria and Goodman, Tom and Abdlkarim, Diar and Di Luca, Massimiliano and Wing, Alan and Hockman, Jason},
  journal={arXiv preprint arXiv:2211.08848},
  year={2022}
}