Spectral subtraction-based speech enhancement for cochlear implant patients in background noise.

By Li-ping Yang, Qian-Jie Fu. 2005

145 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, 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 Cognitive Neuroscience

1. Oliver Y. Chen
2. Huy P Phan
3. M. de Vos
4. Antonio Bonafonte
5. Joan Serrà
6. Hemant A. Patil
7. Neil Shah
8. Santiago Pascual

In Computational Mechanics

1. Antonio Bonafonte
2. Joan Serrà
3. Hemant A. Patil
4. Neil Shah
5. Santiago Pascual
6. Meet H. Soni

In Signal Processing

1. Hemant A. Patil
2. Neil Shah
3. Meet H. Soni
4. Santiago Pascual
5. Joan Serrà
6. M. de Vos
7. Ian Vince McLoughlin
8. Antonio Bonafonte

Citations in the corpus

• SEGAN: Speech Enhancement Generative Adversarial Network (2017, background): “However, we also find important applications related to hearing aids and cochlear implants, where enhancing the signal before amplification can significantly reduce discomfort and increase intelligibility [2].”
• Improving GANs for Speech Enhancement (2020, background): “THE goal of speech enhancement is to improve the quality and intelligibility of speech which are degraded by background noise [1], [2].”
• Time-Frequency Masking-Based Speech Enhancement Using Generative Adversarial Network (2018, background): “The enhanced speech can be used in hearing aid devices and cochlear implant (CI) designs [3].”