Speech enhancement with missing data techniques using recurrent neural networks

By Shahla Parveen, Phil D. Green. 2004

75 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. Aníbal R. Figueiras-Vidal
2. Jordi Pons
3. Xavier Serra
4. Antonio Bonafonte
5. Joan Serrà
6. Santiago Pascual
7. Ali H. Sayed

In Signal Processing

1. Jordi Pons
2. Santiago Pascual
3. Joan Serrà
4. Xavier Serra
5. Dario Rethage
6. Aníbal R. Figueiras-Vidal
7. José-Luis Sancho-Gómez
8. Pedro J. García-Laencina

Citations in the corpus

• A Wavenet for Speech Denoising (2017, background): “Similarly, most techniques for speech denoising use magnitude spectrograms as front-end [3, 4, 5].”
• SEGAN: Speech Enhancement Generative Adversarial Network (2017, background): “Recently, the denoising auto-encoder architecture [13] has been widely adopted.”
• Pattern classification with missing data: a review (2010, background): “corrupted by very high levels of noise are also treated as missing data [16, 17].”