How to use essentia - 10 common examples

def create_analyzers(fs=44100.0,
                     nhop=512,
                     nffts=[1024, 2048, 4096],
                     mel_nband=80,
                     mel_freqlo=27.5,
                     mel_freqhi=16000.0):
    analyzers = []
    for nfft in nffts:
        window = Windowing(size=nfft, type='blackmanharris62')
        spectrum = Spectrum(size=nfft)
        mel = MelBands(inputSize=(nfft // 2) + 1,
                       numberBands=mel_nband,
                       lowFrequencyBound=mel_freqlo,
                       highFrequencyBound=mel_freqhi,
                       sampleRate=fs)
        analyzers.append((window, spectrum, mel))
    return analyzers

def create_analyzers(fs=44100.0,
                     nhop=512,
                     nffts=[1024, 2048, 4096],
                     mel_nband=80,
                     mel_freqlo=27.5,
                     mel_freqhi=16000.0):
    analyzers = []
    for nfft in nffts:
        window = Windowing(size=nfft, type='blackmanharris62')
        spectrum = Spectrum(size=nfft)
        mel = MelBands(inputSize=(nfft // 2) + 1,
                       numberBands=mel_nband,
                       lowFrequencyBound=mel_freqlo,
                       highFrequencyBound=mel_freqhi,
                       sampleRate=fs)
        analyzers.append((window, spectrum, mel))
    return analyzers

#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/felizes/felizes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    print(music)
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/felizes/'+music)

    # See all feature names in the pool in a sorted order
    lista.append(features['tonal.chords_strength.mean'])
    print(music, features['tonal.chords_strength.mean'])

arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_felizes.csv','r')
musics = arq.readlines()
arq.close()

count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_felizes.csv','w')
for m in musics:
    music, erro = m.split("\n",1)
    print(music+","+str(lista[count])+"\n")

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    for i in range(36):
        print(music, features['tonal.hpcp.mean'][i])
        lista.append(features['tonal.hpcp.mean'][i])

arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')
for m in musics:

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    print(music, features['lowlevel.spectral_flux.mean'])
    lista.append(features['lowlevel.spectral_flux.mean'])

arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')
for m in musics:
    music, erro = m.split("\n",1)

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    for i in range(6):
        print(music, features['lowlevel.spectral_contrast_valleys.mean'][i])
        lista.append(features['lowlevel.spectral_contrast_valleys.mean'][i])

arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')
for m in musics:

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    print(music,features['lowlevel.dissonance.mean'])
    lista.append(features['lowlevel.dissonance.mean'])

arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')
for m in musics:
    music, erro = m.split("\n",1)

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    print(music,features['lowlevel.dynamic_complexity'])
    lista.append(features['lowlevel.dynamic_complexity'])



arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    #carregamento dos arquivos
    music, erro = l.split("\n",1)
    #VERIFIQUE O CAMINHO, POR FAVOR
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    print(music, features['lowlevel.spectral_spread.mean'])
    lista.append(features['lowlevel.spectral_spread.mean'])

arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')
for m in musics:
    music, erro = m.split("\n",1)

# 1. Get the file path to the included audio example

# 2. Load the audio as a waveform `y`
#    Store the sampling rate as `sr`
#captura da musica
arq = open('/home/douglas/Música/musicas/wav/tristes/tristes.txt','r')
lines = arq.readlines()
arq.close()

lista = []

count=0
for l in lines:
    music, erro = l.split("\n",1)
    # Loading audio file
    features, features_frames = es.MusicExtractor(lowlevelStats=['mean', 'stdev'],
                                              rhythmStats=['mean', 'stdev'],
                                              tonalStats=['mean', 'stdev'])('/home/douglas/Música/musicas/wav/tristes/'+music)

    # See all feature names in the pool in a sorted order
    print(music, lista.append(features['lowlevel.barkbands_flatness_db.mean']))


arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','r')
musics = arq.readlines()
arq.close()


count=0
arq = open('/home/douglas/Documentos/tcc_code/resultado/resultados_tristes.csv','w')
for m in musics:
    music, erro = m.split("\n",1)