A multi-band spectral subtraction method for enhancing speech corrupted by colored noise

By S. Kamath, P. Loizou. 2002

633 citations recorded by the source corpus.

Subfields: Computational Mechanics, 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 Computational Mechanics

1. P. Loizou
2. Y. Hu
3. Deliang Wang
4. Gibak Kim
5. Yuxuan Wang
6. Ross Cutler
7. Babak Naderi
8. Simon Doclo

In Signal Processing

1. Y. Hu
2. P. Loizou
3. Deliang Wang
4. Gibak Kim
5. Yuxuan Wang
6. Ross Cutler
7. Yu Tsao
8. Babak Naderi

Citations in the corpus

• SEANet: A Multi-modal Speech Enhancement Network (2020, neutral): “Traditional signal processing methods adopt spectral noise subtraction [1, 2], spectral masking [3, 4], statistical methods based on Wiener filtering [5] and Bayesian estimators [6, 7].”
• Subjective Comparison of Speech Enhancement Algorithms (2006, methodology): “Of the two spectral-subtractive algorithms tested, the multi-band spectral subtraction algorithm [15] performed consistently the best across all conditions.”
• Subjective comparison and evaluation of speech enhancement algorithms (2007, methodology): “With the exception of the multi-band (MB) spectral subtraction algorithm developed in our lab (Kamath and Loizou, 2002; Kamath, 2001), the remaining algorithms have been well documented and referenced in the literature.”