Ensemble: A Hybrid Human-Machine System for Generating Melody Scores From Audio

Music transcription is a highly complex task that is difficult for automated algorithms, and equally challenging to people, even those with many years of musical training. Furthermore, there is a shortage of high-quality datasets for training automated transcription algorithms. In this research, we explore a semi-automated, crowdsourced approach to generate music transcriptions, by first running an automatic melody transcription algorithm on a (polyphonic) song to produce a series of discrete notes representing the melody, and then soliciting the crowd to correct this melody. We present a novel web-based interface that enables the crowd to correct transcriptions, report results from an experiment to understand the capabilities of non-experts to perform this challenging task, and characterize the characteristics and actions of workers and how they correlate with transcription performance.

For further details check out our paper:

Ensemble: A Hybrid Human-Machine System for Generating Melody Scores From Audio
T. Tse, J. Salamon, A. Williams, H. Jiang and E. Law
Proc. 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York City, USA, Aug. 2016.
[ISMIR][PDF][BibTex]

Screenshot of the Ensemble interface