def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) if (params['FFTLength'] == '0'): params['FFTLength'] = params['blockSize'] dataflow_safe_append(df, 'FFT', params) dataflow_safe_append(df, 'Abs', {}) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) if params["FFTLength"] == "0": params["FFTLength"] = params["blockSize"] dataflow_safe_append(df, "FFT", params) dataflow_safe_append(df, "Abs", {}) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) if (params['FFTLength'] == '0'): params['FFTLength'] = params['blockSize'] dataflow_safe_append(df, 'FFT', params) dataflow_safe_append(df, 'Abs', {}) return df
def get_dataflow(cls, params, samplerate): nbCoeffs = int(params.get('LSFNbCoeffs')) displacement = int(params.get('LSFDisplacement')) lpcparams = LPC.filter_params(params) lpcparams['LPCNbCoeffs'] = nbCoeffs + 1 - max(displacement, 1) df = LPC.get_dataflow(lpcparams, samplerate) dataflow_safe_append(df, 'LPC2LSF', params) return df
def get_dataflow(cls, params, samplerate): nbCoeffs = int(params.get('LSFNbCoeffs')) displacement = int(params.get('LSFDisplacement')) lpcparams = LPC.filter_params(params) lpcparams['LPCNbCoeffs'] = nbCoeffs + 1 - max(displacement, 1) df = LPC.get_dataflow(lpcparams, samplerate) dataflow_safe_append(df, 'LPC2LSF', params) return df
def get_dataflow(cls, params, samplerate): minFreq = float(params["CQTMinFreq"]) bins = int(params["CQTBinsPerOctave"]) Q = 2.0 / (pow(2.0, 1.0 / bins) - 1) fftLen = Q * samplerate / minFreq fftLen = pow(2, math.ceil(math.log(fftLen) / math.log(2))) fParams = Frames.filter_params(params) fParams["blockSize"] = "%i" % fftLen df = Frames.get_dataflow(fParams, samplerate) dataflow_safe_append(df, "FFT", {"FFTLength": "%i" % fftLen, "FFTWindow": "None"}) dataflow_safe_append(df, "CQT", params) return df
def get_dataflow(cls, params, samplerate): minFreq = float(params['CQTMinFreq']) bins = int(params['CQTBinsPerOctave']) Q = 2.0 / (pow(2.0, 1.0 / bins) - 1) fftLen = Q * samplerate / minFreq fftLen = pow(2, math.ceil(math.log(fftLen) / math.log(2))) fParams = Frames.filter_params(params) fParams['blockSize'] = '%i' % fftLen df = Frames.get_dataflow(fParams, samplerate) dataflow_safe_append( df, 'FFT', {'FFTLength': '%i' % fftLen, 'FFTWindow': 'None'}) dataflow_safe_append(df, 'CQT', params) return df
def get_dataflow(cls, params, samplerate): minFreq = float(params['CQTMinFreq']) bins = int(params['CQTBinsPerOctave']) Q = 2.0 / (pow(2.0, 1.0 / bins) - 1) fftLen = Q * samplerate / minFreq fftLen = pow(2, math.ceil(math.log(fftLen) / math.log(2))) fParams = Frames.filter_params(params) fParams['blockSize'] = '%i' % fftLen df = Frames.get_dataflow(fParams, samplerate) dataflow_safe_append( df, 'FFT', {'FFTLength': '%i' % fftLen, 'FFTWindow': 'None'}) dataflow_safe_append(df, 'CQT', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow(MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, "Sqr", {}) dataflow_safe_append(df, "Loudness", params) if params["LMode"] == "Relative": dataflow_safe_append(df, "Normalize", {"NNorm": "Sum"}) elif params["LMode"] == "Total": dataflow_safe_append(df, "Sum", {}) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'Loudness', params) if (params['LMode'] == 'Relative'): dataflow_safe_append(df, 'Normalize', {'NNorm': 'Sum'}) elif (params['LMode'] == 'Total'): dataflow_safe_append(df, 'Sum', {}) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'Loudness', params) if (params['LMode'] == 'Relative'): dataflow_safe_append(df, 'Normalize', {'NNorm': 'Sum'}) elif (params['LMode'] == 'Total'): dataflow_safe_append(df, 'Sum', {}) return df
def get_dataflow(cls, params, samplerate): tuning = float(params['CZTuning']) fmin = float(params['CQTMinFreq']) if tuning > 0: # adjust min freq to a divisor of tuning b = int(params['CQTBinsPerOctave']) if (b % 12 != 0): print 'WARNING: in Chroma2, CQTBinsPerOctave must be multiple of 12' b = b - b % 12 if b == 0: b = 12 print 'use CQTBinsPerOctave=%i' % b dev = b * math.log(tuning / fmin) / math.log(2) fmin *= pow(2.0, math.fmod(dev, 1) / b) params['CQTMinFreq'] = str(fmin) df = CQT.get_dataflow(CQT.filter_params(params), samplerate) chParams = params del chParams['stepSize'] dataflow_safe_append(df, 'Chroma2', chParams) return df
def get_dataflow(cls, params, samplerate): tuning = float(params["CZTuning"]) fmin = float(params["CQTMinFreq"]) if tuning > 0: # adjust min freq to a divisor of tuning b = int(params["CQTBinsPerOctave"]) if b % 12 != 0: print "WARNING: in Chroma2, CQTBinsPerOctave must be multiple of 12" b = b - b % 12 if b == 0: b = 12 print "use CQTBinsPerOctave=%i" % b dev = b * math.log(tuning / fmin) / math.log(2) fmin *= pow(2.0, math.fmod(dev, 1) / b) params["CQTMinFreq"] = str(fmin) df = CQT.get_dataflow(CQT.filter_params(params), samplerate) chParams = params del chParams["stepSize"] dataflow_safe_append(df, "Chroma2", chParams) return df
def get_dataflow(cls, params, samplerate): tuning = float(params['CZTuning']) fmin = float(params['CQTMinFreq']) if tuning > 0: # adjust min freq to a divisor of tuning b = int(params['CQTBinsPerOctave']) if (b % 12 != 0): print('WARNING: in Chroma2, CQTBinsPerOctave must be multiple of 12') b = b - b % 12 if b == 0: b = 12 print('use CQTBinsPerOctave=%i' % b) dev = b * math.log(tuning / fmin) / math.log(2) fmin *= pow(2.0, math.fmod(dev, 1) / b) params['CQTMinFreq'] = str(fmin) df = CQT.get_dataflow(CQT.filter_params(params), samplerate) chParams = params del chParams['stepSize'] dataflow_safe_append(df, 'Chroma2', chParams) return df
def get_dataflow(cls, params, samplerate): df = OnsetDetectionFunction.get_dataflow(OnsetDetectionFunction.filter_params(params), samplerate) dataflow_safe_append( df, "AutoCorrelationPeaksIntegrator", { "NbFrames": params["BHSBeatFrameSize"], "StepNbFrames": params["BHSBeatFrameStep"], "ACPNbPeaks": params["ACPNbPeaks"], "ACPNorm": "BPM", "ACPInterPeakMinDist": "5", }, ) dataflow_safe_append( df, "HistogramIntegrator", { "NbFrames": params["BHSHistogramFrameSize"], "StepNbFrames": params["BHSHistogramFrameStep"], "HInf": params["HInf"], "HSup": params["HSup"], "HNbBins": params["HNbBins"], "HWeighted": "1", }, ) dataflow_safe_append(df, "HistogramSummary", params) return df
def get_dataflow(cls, params, samplerate): if params["ChordsUse7"] == "1": chtype = "maj,min,7" else: chtype = "maj,min" df = Chroma.get_dataflow( {"CQTMinFreq": "73.42", "CQTNbOctaves": "3", "CQTBinsPerOctave": "36", "stepSize": params["stepSize"]}, samplerate, ) dataflow_safe_append(df, "Chroma2ChordDict", {"ChordTypes": chtype, "ChordNbHarmonics": "1"}) dataflow_safe_append(df, "MedianFilter", {"MFOrder": params["ChordsSmoothing"]}) dataflow_safe_append(df, "ChordDictDecoder", {"ChordTypes": chtype}) return df
def get_dataflow(cls, params, samplerate): b = int(params["CQTBinsPerOctave"]) if b % 12 != 0: print "WARNING: in Chroma, CQTBinsPerOctave must be multiple of 12" b = b - b % 12 if b == 0: b = 12 print "use CQTBinsPerOctave=%i" % b params["CQTBinsPerOctave"] = b df = CQT.get_dataflow(CQT.filter_params(params), samplerate) dataflow_safe_append(df, "ChromaTune", params) dataflow_safe_append(df, "MedianFilter", {"MFOrder": params["ChromaSmoothing"]}) dataflow_safe_append(df, "ChromaReduce", {}) return df
def get_dataflow(cls, params, samplerate): b = int(params['CQTBinsPerOctave']) if (b % 12 != 0): print 'WARNING: in Chroma, CQTBinsPerOctave must be multiple of 12' b = b - b % 12 if b == 0: b = 12 print 'use CQTBinsPerOctave=%i' % b params['CQTBinsPerOctave'] = b df = CQT.get_dataflow(CQT.filter_params(params), samplerate) dataflow_safe_append(df, 'ChromaTune', params) dataflow_safe_append( df, 'MedianFilter', {'MFOrder': params['ChromaSmoothing']}) dataflow_safe_append(df, 'ChromaReduce', {}) return df
def get_dataflow(cls, params, samplerate): b = int(params['CQTBinsPerOctave']) if (b % 12 != 0): print('WARNING: in Chroma, CQTBinsPerOctave must be multiple of 12') b = b - b % 12 if b == 0: b = 12 print('use CQTBinsPerOctave=%i' % b) params['CQTBinsPerOctave'] = b df = CQT.get_dataflow(CQT.filter_params(params), samplerate) dataflow_safe_append(df, 'ChromaTune', params) dataflow_safe_append( df, 'MedianFilter', {'MFOrder': params['ChromaSmoothing']}) dataflow_safe_append(df, 'ChromaReduce', {}) return df
def get_dataflow(cls, params, samplerate): if (params['ChordsUse7'] == '1'): chtype = 'maj,min,7' else: chtype = 'maj,min' df = Chroma.get_dataflow({'CQTMinFreq': '73.42', 'CQTNbOctaves': '3', 'CQTBinsPerOctave': '36', 'stepSize': params['stepSize']}, samplerate) dataflow_safe_append(df, 'Chroma2ChordDict', {'ChordTypes': chtype, 'ChordNbHarmonics': '1'}) dataflow_safe_append(df, 'MedianFilter', {'MFOrder': params['ChordsSmoothing']}) dataflow_safe_append(df, 'ChordDictDecoder', {'ChordTypes': chtype}) return df
def get_dataflow(cls, params, samplerate): if (params['ChordsUse7'] == '1'): chtype = 'maj,min,7' else: chtype = 'maj,min' df = Chroma.get_dataflow({'CQTMinFreq': '73.42', 'CQTNbOctaves': '3', 'CQTBinsPerOctave': '36', 'stepSize': params['stepSize']}, samplerate) dataflow_safe_append(df, 'Chroma2ChordDict', {'ChordTypes': chtype, 'ChordNbHarmonics': '1'}) dataflow_safe_append(df, 'MedianFilter', {'MFOrder': params['ChordsSmoothing']}) dataflow_safe_append(df, 'ChordDictDecoder', {'ChordTypes': chtype}) return df
def get_dataflow(cls, params, samplerate): df = OnsetDetectionFunction.get_dataflow( OnsetDetectionFunction.filter_params(params), samplerate) dataflow_safe_append(df, 'AutoCorrelationPeaksIntegrator', {'NbFrames': params['BHSBeatFrameSize'], 'StepNbFrames': params['BHSBeatFrameStep'], 'ACPNbPeaks': params['ACPNbPeaks'], 'ACPNorm': 'BPM', 'ACPInterPeakMinDist': '5'}) dataflow_safe_append(df, 'HistogramIntegrator', {'NbFrames': params['BHSHistogramFrameSize'], 'StepNbFrames': params['BHSHistogramFrameStep'], 'HInf': params['HInf'], 'HSup': params['HSup'], 'HNbBins': params['HNbBins'], 'HWeighted': '1'}) dataflow_safe_append(df, 'HistogramSummary', params) return df
def get_dataflow(cls, params, samplerate): df = OnsetDetectionFunction.get_dataflow( OnsetDetectionFunction.filter_params(params), samplerate) dataflow_safe_append(df, 'AutoCorrelationPeaksIntegrator', {'NbFrames': params['BHSBeatFrameSize'], 'StepNbFrames': params['BHSBeatFrameStep'], 'ACPNbPeaks': params['ACPNbPeaks'], 'ACPNorm': 'BPM', 'ACPInterPeakMinDist': '5'}) dataflow_safe_append(df, 'HistogramIntegrator', {'NbFrames': params['BHSHistogramFrameSize'], 'StepNbFrames': params['BHSHistogramFrameStep'], 'HInf': params['HInf'], 'HSup': params['HSup'], 'HNbBins': params['HNbBins'], 'HWeighted': '1'}) dataflow_safe_append(df, 'HistogramSummary', params) return df
def get_dataflow(cls, params, samplerate): lParams = Loudness.filter_params(params) lParams['LMode'] = 'Relative' df = Loudness.get_dataflow(lParams, samplerate) dataflow_safe_append(df, 'LoudnessSpread', {}) return df
def get_dataflow(cls, params, samplerate): df = Envelope.get_dataflow(Envelope.filter_params(params), samplerate) dataflow_safe_append(df, 'AmplitudeModulation', params) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'Envelope', params) return df
def get_dataflow(cls, params, samplerate): df = DataFlow() dataflow_safe_append(df, 'Cepstrum', params) return df
def get_dataflow(cls, params, samplerate): df = DataFlow() dataflow_safe_append(df, 'AutoCorrelationPeaksIntegrator', params) return df
def get_dataflow(cls, params, samplerate): acparams = AutoCorrelation.filter_params(params) acparams['ACNbCoeffs'] = str(int(params.get('LPCNbCoeffs')) + 1) df = AutoCorrelation.get_dataflow(acparams, samplerate) dataflow_safe_append(df, 'AC2LPC', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'MelFilterBank', params) dataflow_safe_append(df, 'Cepstrum', params) return df
def get_dataflow(cls, params, samplerate): df = OBSI.get_dataflow(OBSI.filter_params(params), samplerate) dataflow_safe_append(df, 'Difference', params) return df
def get_dataflow(cls, params, samplerate): df = DataFlow() dataflow_safe_append(df, 'AutoCorrelationPeaksIntegrator', params) return df
def get_dataflow(cls, params, samplerate): lParams = Loudness.filter_params(params) lParams['LMode'] = 'Relative' df = Loudness.get_dataflow(lParams, samplerate) dataflow_safe_append(df, 'LoudnessSpread', {}) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'FFT', params) dataflow_safe_append(df, 'ComplexDomainFlux', params) return df
def get_dataflow(cls, params, samplerate): df = Envelope.get_dataflow(Envelope.filter_params(params), samplerate) dataflow_safe_append(df, 'AmplitudeModulation', params) return df
def get_dataflow(cls, params, samplerate): df = Envelope.get_dataflow(Envelope.filter_params(params), samplerate) dataflow_safe_append(df, 'ShapeStatistics', params) return df
def get_dataflow(cls, params, samplerate): df = OBSI.get_dataflow(OBSI.filter_params(params), samplerate) dataflow_safe_append(df, 'Difference', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'NormalizeMaxAll', params) dataflow_safe_append( df, 'FilterSmallValues', {'FSVThreshold': '0.001'}) dataflow_safe_append(df, 'HalfHannFilter', {'HHFOrder': '0.175s'}) dataflow_safe_append(df, 'LogCompression', {}) dataflow_safe_append( df, 'DvornikovDifferentiator', {'DDOrder': '0.08s'}) dataflow_safe_append(df, 'FilterSmallValues', {'FSVThreshold': '1'}) dataflow_safe_append(df, 'Sum', {}) dataflow_safe_append(df, 'NormalizeMaxAll', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'NormalizeMaxAll', params) dataflow_safe_append( df, 'FilterSmallValues', {'FSVThreshold': '0.001'}) dataflow_safe_append(df, 'HalfHannFilter', {'HHFOrder': '0.175s'}) dataflow_safe_append(df, 'LogCompression', {}) dataflow_safe_append( df, 'DvornikovDifferentiator', {'DDOrder': '0.08s'}) dataflow_safe_append(df, 'FilterSmallValues', {'FSVThreshold': '1'}) dataflow_safe_append(df, 'Sum', {}) dataflow_safe_append(df, 'NormalizeMaxAll', params) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'AutoCorrelation', params) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'RMS', {}) return df
def get_dataflow(cls, params, samplerate): df = CQT.get_dataflow(params, samplerate) dataflow_safe_append(df, 'Difference', {'DiffNbCoeffs': '0'}) return df
def get_dataflow(cls, params, samplerate): acparams = AutoCorrelation.filter_params(params) acparams['ACNbCoeffs'] = str(int(params.get('LPCNbCoeffs')) + 1) df = AutoCorrelation.get_dataflow(acparams, samplerate) dataflow_safe_append(df, 'AC2LPC', params) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'FFT', params) dataflow_safe_append(df, 'ComplexDomainFlux', params) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'AutoCorrelation', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'MelFilterBank', params) dataflow_safe_append(df, 'Cepstrum', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow(params, samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'SpectralCrestFactorPerBand', params) return df
def get_dataflow(cls, params, samplerate): df = Envelope.get_dataflow(Envelope.filter_params(params), samplerate) dataflow_safe_append(df, 'ShapeStatistics', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow(params, samplerate) dataflow_safe_append(df, 'Decrease', params) return df
def get_dataflow(cls, params, samplerate): # Power Spectrum df = yf.MagnitudeSpectrum.get_dataflow( yf.MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'WindowNormalize', {'NormWindow': 'Hanning'}) dataflow_safe_append(df, 'Sqr', {}) #dataflow_safe_append(df,'DCOffsetFilter',{}) #only good for special files dataflow_safe_append(df, 'DBConversion', {}) # Emphasize Local Peaks dataflow_safe_append(df, 'SubRunningAverage', params) # Binarization dataflow_safe_append(df, 'Binarization', params) # Calculate Frequency Activation dataflow_safe_append(df, 'FrameSum', params) # Detect Strong Peaks dataflow_safe_append(df, 'PeakDetection', params) # Quantify CFA dataflow_safe_append(df, 'Sum', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow(params, samplerate) dataflow_safe_append(df, 'ShapeStatistics', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'OBSI', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow(params, samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'Rolloff', params) return df
def get_dataflow(cls, params, samplerate): df = Frames.get_dataflow(Frames.filter_params(params), samplerate) dataflow_safe_append(df, 'ShapeStatistics', {}) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow(params, samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'SpectralCrestFactorPerBand', params) return df
def get_dataflow(cls, params, samplerate): df = MagnitudeSpectrum.get_dataflow( MagnitudeSpectrum.filter_params(params), samplerate) dataflow_safe_append(df, 'Sqr', {}) dataflow_safe_append(df, 'OBSI', params) return df
def get_dataflow(cls, params, samplerate): df = DataFlow() dataflow_safe_append(df, 'Cepstrum', params) return df