Authors

Venue

Date

Share

30+ Years of Source Separation Research: Achievements and Future Challenges

Shoko Araki

Nobutaka Ito

Reinhold Haeb-Umbach

Gordon Wichern

Zhong-Qiu Wang

Yuki Mitsufuji

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

Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space

ICLR, 2025
Yangming Li, Chieh-Hsin Lai, Carola-Bibiane Schönlieb, Yuki Mitsufuji, Stefano Ermon*

Deep Generative Models (DGMs), including Energy-Based Models (EBMs) and Score-based Generative Models (SGMs), have advanced high-fidelity data generation and complex continuous distribution approximation. However, their application in Markov Decision Processes (MDPs), partic…

Training Consistency Models with Variational Noise Coupling

ICLR, 2025
Gianluigi Silvestri, Luca Ambrogioni, Chieh-Hsin Lai, Yuhta Takida, Yuki Mitsufuji

Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and impr…

Classifier-Free Guidance inside the Attraction Basin May Cause Memorization

CVPR, 2025
Anubhav Jain, Yuya Kobayashi, Takashi Shibuya, Yuhta Takida, Nasir Memon, Julian Togelius, Yuki Mitsufuji

Diffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement and/or leakage of privacy-sensitive information. In this paper, we present a novel way to unders…

  • HOME
  • Publications
  • 30+ Years of Source Separation Research: Achievements and Future Challenges

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.