Spectral Subtraction Based on Minimum Statistics

By Rainer Martin. 2001

581 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f94d43706b3b39c7c2fbf5083a34bee23c6bf984

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1. Qiquan Zhang
2. P. Loizou
3. Israel Cohen
4. Sundarrajan Rangachari
5. Kuldip K. Paliwal
6. Aaron Nicolson
7. Mingjiang Wang
8. Baruch Berdugo

Citations in the corpus

• A noise-estimation algorithm for highly non-stationary environments (2006, methodology): “Various methods (Martin, 2001; Martin, 1994) were proposed for tracking the minimum of the noisy speech power spectrum over a fixed search window length.”
• Speech enhancement for non-stationary noise environments (2001, methodology): “Martin [14] [15] has proposed an algorithm for noise estimation based on minimum statistics.”
• The importance of phase in speech enhancement (2011, methodology): “Traditional AMS-based speech enhancement methods (e.g., Lim and Oppenheim, 1979; Berouti et al., 1979; Ephraim and Malah, 1984; Ephraim et al., 1985; Martin, 1994; Sim et al., 1998; Virag, 1999), modify only the noisy magnitude spectrum, while keeping the noisy phase spectrum unchanged.”
• DeepMMSE: A Deep Learning Approach to MMSE-Based Noise Power Spectral Density Estimation (2020, methodology): “A more advanced noise tracking method is minimum statistics (MS) [13], [14].”
• Noise power spectral density estimation based on optimal smoothing and minimum statistics (2001, methodology): “Based on our previous work [7] and the results obtained by others [9] we have extended the minimum statistics noise estimation approach to improve its performance in nonstationary noise.”
• Noise estimation by minima controlled recursive averaging for robust speech enhancement (2002, methodology): “However, this noise estimate is sensitive to outliers [5] and its variance is about twice as large as the variance of a conventional noise estimator [6].”
• Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging (2003, methodology): “Digital Object Identifier 10.1109/TSA.2003.811544 A useful noise estimation approach, known as the minimum statistics (MS) [12], is to track the minima values of a smoothed power estimate of the noisy signal, and multiply the result by a factor that compensates the bias.”