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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import scipy.signal as sig
import copy
import librosa
def bandpower(ps, mode="time"):
"""
estimate bandpower, see https://de.mathworks.com/help/signal/ref/bandpower.html
"""
if mode == "time":
x = ps
l2norm = np.linalg.norm(x) ** 2.0 / len(x)
return l2norm
elif mode == "psd":
return sum(ps)
def getIndizesAroundPeak(arr, peakIndex, searchWidth=1000):
peakBins = []
magMax = arr[peakIndex]
curVal = magMax
for i in range(searchWidth):
newBin = peakIndex + i
if newBin >= len(arr):
break
newVal = arr[newBin]
if newVal > curVal:
break
else:
peakBins.append(int(newBin))
curVal = newVal
curVal = magMax
for i in range(searchWidth):
newBin = peakIndex - i
if newBin < 0:
break
newVal = arr[newBin]
if newVal > curVal:
break
else:
peakBins.append(int(newBin))
curVal = newVal
return np.array(list(set(peakBins)))
def freqToBin(fAxis, Freq):
return np.argmin(abs(fAxis - Freq))
def getPeakInArea(psd, faxis, estimation, searchWidthHz=10):
"""
returns bin and frequency of the maximum in an area
"""
binLow = freqToBin(faxis, estimation - searchWidthHz)
binHi = freqToBin(faxis, estimation + searchWidthHz)
peakbin = binLow + np.argmax(psd[binLow : binHi + 1])
return peakbin, faxis[peakbin]
def getHarmonics(fund, sr, nHarmonics=6, aliased=False):
harmonicMultipliers = np.arange(2, nHarmonics + 2)
harmonicFs = fund * harmonicMultipliers
if not aliased:
harmonicFs[harmonicFs > sr / 2] = -1
harmonicFs = np.delete(harmonicFs, harmonicFs == -1)
else:
nyqZone = np.floor(harmonicFs / (sr / 2))
oddEvenNyq = nyqZone % 2
harmonicFs = np.mod(harmonicFs, sr / 2)
harmonicFs[oddEvenNyq == 1] = (sr / 2) - harmonicFs[oddEvenNyq == 1]
return harmonicFs
def extract_snr(audio, sr=None):
"""Extract Signal-to-Noise Ratio for a given audio."""
if sr != None:
audio, _ = librosa.load(audio, sr=sr)
else:
audio, sr = librosa.load(audio, sr=sr)
faxis, ps = sig.periodogram(
audio, fs=sr, window=("kaiser", 38)
) # get periodogram, parametrized like in matlab
fundBin = np.argmax(
ps
) # estimate fundamental at maximum amplitude, get the bin number
fundIndizes = getIndizesAroundPeak(
ps, fundBin
) # get bin numbers around fundamental peak
fundFrequency = faxis[fundBin] # frequency of fundamental
nHarmonics = 18
harmonicFs = getHarmonics(
fundFrequency, sr, nHarmonics=nHarmonics, aliased=True
) # get harmonic frequencies
harmonicBorders = np.zeros([2, nHarmonics], dtype=np.int16).T
fullHarmonicBins = np.array([], dtype=np.int16)
fullHarmonicBinList = []
harmPeakFreqs = []
harmPeaks = []
for i, harmonic in enumerate(harmonicFs):
searcharea = 0.1 * fundFrequency
estimation = harmonic
binNum, freq = getPeakInArea(ps, faxis, estimation, searcharea)
harmPeakFreqs.append(freq)
harmPeaks.append(ps[binNum])
allBins = getIndizesAroundPeak(ps, binNum, searchWidth=1000)
fullHarmonicBins = np.append(fullHarmonicBins, allBins)
fullHarmonicBinList.append(allBins)
harmonicBorders[i, :] = [allBins[0], allBins[-1]]
fundIndizes.sort()
pFund = bandpower(ps[fundIndizes[0] : fundIndizes[-1]]) # get power of fundamental
noisePrepared = copy.copy(ps)
noisePrepared[fundIndizes] = 0
noisePrepared[fullHarmonicBins] = 0
noiseMean = np.median(noisePrepared[noisePrepared != 0])
noisePrepared[fundIndizes] = noiseMean
noisePrepared[fullHarmonicBins] = noiseMean
noisePower = bandpower(noisePrepared)
r = 10 * np.log10(pFund / noisePower)
return r, 10 * np.log10(noisePower)
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