Time-Domain Blind Separation of Audio Sources on the Basis of a Complete ICA Decomposition of an Observation Space

By Zbyněk Koldovský, Petr Tichavský. 2011

62 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yi Luo
2. Nima Mesgarani

In Signal Processing

1. Yi Luo
2. Nima Mesgarani
3. Yossi Adi
4. Jonathan Le Roux
5. Zhuo Chen
6. Lior Wolf
7. Eliya Nachmani
8. Takuya Yoshioka

Citations in the corpus

• Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation (2018, background): “ity of the target and interfering sources. This aspect, however, limits their applicability when all the sources are speech signals and therefore have similar statistical properties [35], [36], [37], [38]. Beyond ICA methods, several recent studies have investigated the feasibility of time-domain se”
• Global and Local Simplex Representations for Multichannel Source Separation (2020, unknown): “Assuming independence of the original source signals facilitates the use of independent component analysis (ICA) and independent vector analysis (IVA) separation methods [5]–[9].”