Authors
- Shoko Araki
- Nobutaka Ito
- Reinhold Haeb-Umbach
- Gordon Wichern
- Zhong-Qiu Wang
- Yuki Mitsufuji
Venue
- ICASSP-25
Date
- 2025
Share
30+ Years of Source Separation Research: Achievements and Future Challenges
Shoko Araki
Nobutaka Ito
Reinhold Haeb-Umbach
Gordon Wichern
Zhong-Qiu Wang
ICASSP-25
2025
Abstract
Source separation (SS) of acoustic signals is a research field that emerged in the mid-1990s and has flourished ever since. On the occasion of ICASSP's 50th anniversary, we review the major contributions and advancements in the past three decades in the speech, audio, and music SS research field. We will cover both single- and multi-channel SS approaches. We will also look back on key efforts to foster a culture of scientific evaluation in the research field, including challenges, performance metrics, and datasets. We will conclude by discussing current trends and future research directions.
Related Publications
We present Music Arena, an open platform for scalable human preference evaluation of text-to-music (TTM) models. Soliciting human preferences via listening studies is the gold standard for evaluation in TTM, but these studies are expensive to conduct and difficult to compare…
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specific training data points contributed to the generation of a particular output from a specific mod…
Blind inverse problems, where both the target data and forward operator are unknown, are crucial to many computer vision applications. Existing methods often depend on restrictive assumptions such as additional training, operator linearity, or narrow image distributions, thu…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.
