def Easygram(limits, song, bpm, barIn, barEnd, measure, unitSize, save=False):
'''
Easy spectrogram that uses Frequency Peaks and Frequency Bands to simplify the waveform information.
Parameters
----------
limits : TYPE
Receive a list of frequency bands of same length.
song : TYPE
Song object from onset, used to localize the chunk.
bpm : TYPE
Bpm of the song.
barIn : TYPE
Bar Number for chunk start.
barEnd : TYPE
Bar Number for chunk end.
measure : TYPE
Number of beats in bar.
unitSize : TYPE
Grid division by beat size, 1/16 of 1/4 is 0.25.
save : TYPE, optional
Save .csv file for multiband array.
Returns
-------
multiband : TYPE
An array of energy by frequency bands.
energy : TYPE
Total amount of energy in the chunk.
topFrequencies : TYPE
The frequency band(s) with highest energy.
TO DO: Add number of top frequencies > 1, add option to enable PlotPeaks2
'''
multiband, energy, topFrequencies = [], [], []
unitStar = int(barIn * measure * (1 / unitSize))
amountOfUnits = int(barEnd * measure * (1 / unitSize))
unit = (60 / bpm) * song.sampfreq * unitSize
unitRange = 4096
pai = song.peakAlphaIndex
for i in range(unitStar, amountOfUnits):
start, end = pai + int(i * unit), pai + int(i * unit) + unitRange
chunk = song.data[start:end]
freqs, fft = onset.CalculateFFT_dB(chunk, song.sampfreq, limits[0],
limits[len(limits) - 1])
x, y = onset.GetFrequencyPeaks(freqs, fft)
freqBandsAmp = onset.FrequencyBands(x, y, limits)
#onset.PlotPeaks2(limits, freqBandsAmp, limits, (0,250), "plotUnits/" + str(i+1) + ".png")
multiband.append(freqBandsAmp) # energy in bands
energy.append(np.sum(freqBandsAmp)) # total energy in spectrum
topFrequencies.append(onset.GetTopFrequencies(limits, freqBandsAmp, 1))
if save == True:
SaveSimpleSpectrogram(limits, multiband)
return multiband, energy, topFrequencies
def NewOnset(song, limits, chunkSize):
energy, topFrequencies = [], []
unitStart = song.peakAlphaIndex
amountOfUnits = song.length // chunkSize
for i in range(amountOfUnits):
start = unitStart + int(i * chunkSize)
end = start + chunkSize
chunk = song.data[start:end]
freqs, fft = onset.CalculateFFT_dB(chunk, song.sampfreq, limits[0],
limits[len(limits) - 1])
energy.append(np.max(fft))
topFrequencies.append(onset.GetTopFrequencies(freqs, fft, start, 1))
return energy, topFrequencies
def GetSimpleSpectrogram_FromNotes(limits):
multiband = []
for i in range(len(song.notes)):
start, end = song.notes[i][0], song.notes[i][0] + 4096
chunk = song.data[start:end]
freqs, fft = onset.CalculateFFT_dB(chunk, song.sampfreq, limits[0], limits[len(limits)-1])
#x, y = onset.GetSpectrumPeaks(freqs, fft)
x, y = freqs, fft
freqBandsAmp = onset.FrequencyBands(x,y,limits)
onset.PlotPeaks2(limits, freqBandsAmp, limits, (0,300), "plots/" + str(i+1) + ".png")
multiband.append(freqBandsAmp)
onset.SaveSimpleSpectrogram(limits, multiband)
def Easygram_4096(limits, song, bpm, barIn, barEnd, measure, unitSize):
energy, topFrequencies = [], []
unitStar = int(barIn * measure * (1 / unitSize))
amountOfUnits = int(barEnd * measure * (1 / unitSize))
unit = (60 / bpm) * song.sampfreq * unitSize
chunkSize, pai = 4096, song.peakAlphaIndex
for i in range(unitStar, amountOfUnits):
start, end = pai + int(i * unit), pai + int(i * unit) + chunkSize
chunk = song.data[start:end]
freqs, fft = onset.CalculateFFT_dB(chunk, song.sampfreq, limits[0],
limits[len(limits) - 1])
energy.append(np.max(fft)) # max energy point
topFrequencies.append(onset.GetTopFrequencies(freqs, fft, start, 1))
return energy, topFrequencies
def Easygram(limits, song, bpm, barIn, barEnd, measure, unitSize):
'''
Easy spectrogram that uses Frequency Peaks and Frequency Bands to simplify the waveform information.
Parameters
----------
limits : TYPE
Receive a list of frequency bands of same length.
song : TYPE
Song object from onset, used to localize the chunk.
bpm : TYPE
Bpm of the song.
barIn : TYPE
Bar Number for chunk start.
barEnd : TYPE
Bar Number for chunk end.
measure : TYPE
Number of beats in bar.
unitSize : TYPE
Grid division by beat size, 1/16 of 1/4 is 0.25.
Returns
-------
energy : TYPE
Total amount of energy in the chunk.
topFrequencies : TYPE
The frequency band(s) with highest energy.
TO DO: Add number of top frequencies > 1, add option to enable PlotPeaks2
'''
energy, topFrequencies = [], []
unitStar = int(barIn * measure * (1 / unitSize))
amountOfUnits = int(barEnd * measure * (1 / unitSize))
unit = (60 / bpm) * song.sampfreq * unitSize
unitRange = 2048
pai = song.peakAlphaIndex
for i in range(unitStar, amountOfUnits):
start, end = pai + int(i * unit), pai + int(i * unit) + unitRange
chunk = song.data[start:end]
freqs, fft = onset.CalculateFFT_dB(chunk, song.sampfreq, limits[0],
limits[len(limits) - 1])
energy.append(np.max(fft)) # max energy point
topFrequencies.append(onset.GetTopFrequencies(freqs, fft, start, 1))
return energy, topFrequencies
def EasyOnsets(song, limits, chunkSize, tr):
energy = []
unitStart = song.peakAlphaIndex
amountOfUnits = song.length // chunkSize
for i in range(amountOfUnits):
start = unitStart + int(i * chunkSize)
end = start + chunkSize
chunk = song.data[start:end]
freqs, fft = onset.CalculateFFT_dB(chunk, song.sampfreq, limits[0],
limits[len(limits) - 1])
energy.append(np.max(np.absolute(fft)))
threshold = (max(energy) - min(energy)) * tr + min(energy)
notes = []
for i in range(len(energy)):
if energy[i] > threshold:
notes.append(energy[i])
return notes
def SampleProfile(file, resolution):
audio = onset.Song(wavfile.read("songs/samples/" + file))
limits = [240,1680]
note = audio.data[0:resolution]
onset.PlotNote(note, audio.sampfreq, limits[0], limits[len(limits)-1], file.split(".")[0] + ".png")
freqs, fft = onset.CalculateFFT_dB(note, audio.sampfreq, limits[0], limits[len(limits)-1])
x, y = onset.GetSpectrumPeaks(freqs, fft)
print(x,y)
freq, amp = onset.GetTopFrequencies(x,y,8)
plt.figure(figsize = (20,10))
plt.xticks(freq)
plt.scatter(freq,amp)
plt.xlim(limits[0],limits[1])
plt.ylim(10,30)
plt.savefig("sp_" + file.split(".")[0] + ".png")
plt.show()
def GetBeatFrequencySnapshot(x, chunkSize, filterLow, filterHigh):
chunksInData = len(x) // chunkSize
frequencies = np.empty(0)
energies = np.empty(0)
for i in range(chunksInData - 1):
start = int(i * chunkSize)
end = int(i * chunkSize) + chunkSize
chunk = x[start:end]
freq, fft = onset.CalculateFFT_dB(chunk, sr, filterLow, filterHigh)
energy = np.sum(fft)
frequency = np.average(freq, weights=fft)
energies = np.append(energies, energy)
frequencies = np.append(frequencies, frequency)
freq = np.average(frequencies)
power = np.average(energies)
#index = np.where(frequencies == mode)
#np.average(frequencies)
return np.array([freq, power])
