Daniel Stoller, Mi Tian, Sebastian Ewert, and Simon Dixon
Revisiting Singing Voice Detection: A Quantitative Review and the Future Outlook
Since the vocal component plays a crucial role in popular music, singing voice detection has been an active research topic in music information retrieval. Although several proposed algorithms have shown high performances, we argue that there still is a room to improve to build a more robust singing voice detection system. In order to identify the area of improvement, we first perform an error analysis on three recent singing voice detection systems. Based on the analysis, we design novel methods to test the systems on multiple sets of internally curated and generated data to further examine the pitfalls, which are not clearly revealed with the current datasets. From the experiment results, we also propose several directions towards building a more robust singing voice detector.
Using a Neural Network Codec Approximation Loss to Improve Source Separation Performance in Limited Capacity Networks
Ishwarya Ananthabhotla, Sebastian Ewert, Joseph A. Paradiso
Rachel M Bittner, Magdalena Fuentes, David Rubinstein, Andreas Jansson, Keunwoo Choi, Thor Kell