Speech enhancement based on minimum mean-square error estimation and supergaussian priors

By Rainer Martin. IEEE Transactions on Speech and Audio Processing, 2005

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/cc881fbd6565640670a63aa5663c0d76412b4e8d

How researchers see this paper

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

In Artificial Intelligence

1. Nasser Mohammadiha
2. Arne Leijon
3. Paris Smaragdis
4. Timo Gerkmann
5. Richard Christian Hendriks
6. Jinyu Li

In Computational Mechanics

1. Paris Smaragdis
2. Nasser Mohammadiha
3. Timo Gerkmann
4. Arne Leijon
5. Richard Christian Hendriks
6. Deliang Wang
7. Zheng-Hua Tan
8. Jinyu Li

In Signal Processing

1. Nasser Mohammadiha
2. Paris Smaragdis
3. Arne Leijon
4. Timo Gerkmann
5. Richard Christian Hendriks
6. Deliang Wang
7. John R. Hershey
8. Jonathan Le Roux

Citations in the corpus

• Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay (2012, background): “Based on the observation that the observed distribution of speech spectral coefficients tends to be more super-Gaussian than Gaussian, see, e.g., [4] and [5], further improvements of the estimators presented in [2], [3] were obtained in [4]–[7], where it was proposed to derive Bayesian estimators…”
• Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization (2013, background): “The distribution ofyt is obtained by assuming that different frequency bins are independent [5], [7]:”