protected void update(double[] signal, int samplingRate) { super.updateData(0, 1. / samplingRate, signal); updateSound(signal, samplingRate); }
protected void update(double[] signal, int samplingRate) { super.updateData(0, 1. / samplingRate, signal); updateSound(signal, samplingRate); }
updateData(newX0, newXStep, data);
updateData(newX0, newXStep, data);
if (spectra_indexmax > fftSize / 2) spectra_indexmax = fftSize / 2; // == spectra[i].length super.updateData(0, (double) windowShift / samplingRate, new double[spectra.size()]);
if (spectra_indexmax > fftSize / 2) spectra_indexmax = fftSize / 2; // == spectra[i].length super.updateData(0, (double) windowShift / samplingRate, new double[spectra.size()]);
if (cepstra_indexmax > cepstrumAnalyser.getInverseFFTWindowLength() / 2) cepstra_indexmax = cepstrumAnalyser.getInverseFFTWindowLength() / 2; // == cepstra[i].length super.updateData(0, (double) windowShift / samplingRate, new double[cepstra.size()]);
if (cepstra_indexmax > cepstrumAnalyser.getInverseFFTWindowLength() / 2) cepstra_indexmax = cepstrumAnalyser.getInverseFFTWindowLength() / 2; // == cepstra[i].length super.updateData(0, (double) windowShift / samplingRate, new double[cepstra.size()]);
public void update(double x) { if (Double.isNaN(x)) return; int centerIndex = (int) (x * samplingRate); assert centerIndex >= 0 && centerIndex < signal.length; int windowLength = 1024; int leftIndex = centerIndex - windowLength / 2; if (leftIndex < 0) leftIndex = 0; double[] signalExcerpt = new HammingWindow(windowLength).apply(signal, leftIndex); double[] spectrum = FFT.computeLogPowerSpectrum(signalExcerpt); if (graph == null) { graph = new FunctionGraph(300, 200, 0, samplingRate / windowLength, spectrum); } else { graph.updateData(0, samplingRate / windowLength, spectrum); } super.updateGraph(graph, "Spectrum at " + new PrintfFormat("%.3f").sprintf(x) + " s"); }
public void update(double x) { if (Double.isNaN(x)) return; int centerIndex = (int) (x * samplingRate); assert centerIndex >= 0 && centerIndex < signal.length; int windowLength = 1024; int leftIndex = centerIndex - windowLength / 2; if (leftIndex < 0) leftIndex = 0; double[] signalExcerpt = new HammingWindow(windowLength).apply(signal, leftIndex); double[] spectrum = FFT.computeLogPowerSpectrum(signalExcerpt); if (graph == null) { graph = new FunctionGraph(300, 200, 0, samplingRate / windowLength, spectrum); } else { graph.updateData(0, samplingRate / windowLength, spectrum); } super.updateGraph(graph, "Spectrum at " + new PrintfFormat("%.3f").sprintf(x) + " s"); }
for (int i = 0; i < clusters.length; i++) { double[] meanValues = clusters[i].getMeanPolynomial().generatePolynomialValues(100, 0, 1); clusterGraph.updateData(0, 1. / meanValues.length, meanValues);
graph = new FunctionGraph(300, 200, 0, samplingRate, realCepstrum); } else { graph.updateData(0, samplingRate, realCepstrum); cepstrumSpectrumAtCursor = new FunctionGraph(300, 200, 0, samplingRate / real.length, cepstrumSpectrum); } else { cepstrumSpectrumAtCursor.updateData(0, samplingRate / real.length, cepstrumSpectrum);
for (int i = 0; i < clusters.length; i++) { double[] meanValues = clusters[i].getMeanPolynomial().generatePolynomialValues(100, 0, 1); clusterGraph.updateData(0, 1. / meanValues.length, meanValues);
graph = new FunctionGraph(300, 200, 0, samplingRate, realCepstrum); } else { graph.updateData(0, samplingRate, realCepstrum); cepstrumSpectrumAtCursor = new FunctionGraph(300, 200, 0, samplingRate / real.length, cepstrumSpectrum); } else { cepstrumSpectrumAtCursor.updateData(0, samplingRate / real.length, cepstrumSpectrum);
for (int i = 0; i < clusters.length; i++) { double[] meanValues = clusters[i].getMeanPolynomial().generatePolynomialValues(100, 0, 1); clusterGraph.updateData(0, 1. / meanValues.length, meanValues);
for (int i = 0; i < clusters.length; i++) { double[] meanValues = clusters[i].getMeanPolynomial().generatePolynomialValues(100, 0, 1); clusterGraph.updateData(0, 1. / meanValues.length, meanValues);
for (int i = 0; i < clusters.length; i++) { double[] meanValues = clusters[i].getMeanPolynomial().generatePolynomialValues(100, 0, 1); clusterGraph.updateData(0, 1. / meanValues.length, meanValues);
for (int i = 0; i < clusters.length; i++) { double[] meanValues = clusters[i].getMeanPolynomial().generatePolynomialValues(100, 0, 1); clusterGraph.updateData(0, 1. / meanValues.length, meanValues);
graph = new FunctionGraph(300, 200, 0, samplingRate / windowLength, lpcSpectrum); } else { graph.updateData(0, samplingRate / windowLength, lpcSpectrum);
graph = new FunctionGraph(300, 200, 0, samplingRate / windowLength, lpcSpectrum); } else { graph.updateData(0, samplingRate / windowLength, lpcSpectrum);