Examples of edu.cmu.sphinx.frontend.denoise.Denoise

edu.cmu.sphinx.frontend.denoise.Denoise
The noise filter, same as implemented in sphinxbase/sphinxtrain/pocketsphinx. Noise removal algorithm is inspired by the following papers Computationally Efficient Speech Enchancement by Spectral Minina Tracking by G. Doblinger Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition by C. Kim. For the recent research and state of art see papers about IMRCA and A Minimum-Mean-Square-Error Noise Reduction Algorithm On Mel-Frequency Cepstra For Robust Speech Recognition by Dong Yu and others


            selectedDataProcessors.add(filterBank);


            if ((featParams.get("-remove_noise") != null)
                    && (featParams.get("-remove_noise").equals("yes"))) {
                denoise = new Denoise(Denoise.class.getField("LAMBDA_POWER")
                                              .getAnnotation(S4Double.class)
                                              .defaultValue(),
                                      Denoise.class.getField("LAMBDA_A")
                                              .getAnnotation(S4Double.class)
                                              .defaultValue(),

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Related Classes of edu.cmu.sphinx.frontend.denoise.Denoise

edu.cmu.sphinx.frontend.AutoCepstrum

edu.cmu.sphinx.frontend.Data

edu.cmu.sphinx.frontend.DoubleData

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