Speech enhancement based on perceptually motivated bayesian estimators of the magnitude spectrum

By P. Loizou. 2005

289 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, 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. Stefan Uhlich

In Signal Processing

1. Yuki Mitsufuji
2. Stefan Uhlich
3. Franck Giron
4. Naoya Takahashi
5. Nabarun Goswami
6. Alexandre D'efossez
7. Antoine Liutkus
8. Fabian-Robert Stöter

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].”
• Deep neural network based instrument extraction from music (2015, background): “4We also tested the weighted Euclidean distance [19] as it showed good results for speech enhancement.”
• Subjective Comparison of Speech Enhancement Algorithms (2006, unknown): “KLT Eq. 14,48 [8] pKLT Eq. 34, =0.08 [9] MMSE-SPU Eq. 7,51, q=0.3 [10] logMMSE Eq. 20 [11] logMMSE-ne Eq. 20 [11] logMMSE-SPU Eq. 2,8,10,16 [12] pMMSE Eq. 12 [ 13 ] RDC Eq. 6,7,10,14,15 [14] RDC-ne Eq. 6,7,10,14,15 [14] MB Eq. 4-7 [15] WavThr Eq. 11,25 [16] Wiener as Eq. 3-7 [4] AudSup Eq. 26,38, b(”
• Subjective comparison and evaluation of speech enhancement algorithms (2007, unknown): “Loizou). www.elsevier.com/locate/specom Speech Communication 49 (2007) 588–601”