本文整理汇总了Python中librosa.display方法的典型用法代码示例。如果您正苦于以下问题:Python librosa.display方法的具体用法?Python librosa.display怎么用?Python librosa.display使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类librosa
的用法示例。
在下文中一共展示了librosa.display方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: visualize_spectrogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def visualize_spectrogram(audio_signal, ch=0, do_mono=False, x_axis='time',
y_axis='linear', **kwargs):
"""
Wrapper around `librosa.display.specshow` for usage with AudioSignals.
Args:
audio_signal (AudioSignal): AudioSignal to plot
ch (int, optional): Which channel to plot. Defaults to 0.
do_mono (bool, optional): Make the AudioSignal mono. Defaults to False.
x_axis (str, optional): x_axis argument to librosa.display.specshow. Defaults to 'time'.
y_axis (str, optional): y_axis argument to librosa.display.specshow. Defaults to 'linear'.
kwargs: Additional keyword arguments to librosa.display.specshow.
"""
import librosa.display
if do_mono:
audio_signal = audio_signal.to_mono(overwrite=False)
data = librosa.amplitude_to_db(np.abs(audio_signal.stft()), ref=np.max)
librosa.display.specshow(data[..., ch], x_axis=x_axis, y_axis=y_axis,
sr=audio_signal.sample_rate, hop_length=audio_signal.stft_params.hop_length,
**kwargs)
示例2: visualize_waveform
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def visualize_waveform(audio_signal, ch=0, do_mono=False, x_axis='time', **kwargs):
"""
Wrapper around `librosa.display.waveplot` for usage with AudioSignals.
Args:
audio_signal (AudioSignal): AudioSignal to plot
ch (int, optional): Which channel to plot. Defaults to 0.
do_mono (bool, optional): Make the AudioSignal mono. Defaults to False.
x_axis (str, optional): x_axis argument to librosa.display.waveplot. Defaults to 'time'.
kwargs: Additional keyword arguments to librosa.display.waveplot.
"""
import librosa.display
import matplotlib.pyplot as plt
if do_mono:
audio_signal = audio_signal.to_mono(overwrite=False)
data = np.asfortranarray(audio_signal.audio_data[ch])
librosa.display.waveplot(data, sr=audio_signal.sample_rate, x_axis=x_axis, **kwargs)
plt.ylabel('Amplitude')
示例3: save2png
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def save2png(time_step,frame_width,wav,feature_type,num_filters,num_feature_columns,head_folder,delta=False,dom_freq=False,noise_wavefile=None,vad = True):
feats = coll_feats_manage_timestep(time_step,frame_width,wav,feature_type,num_filters,num_feature_columns,head_folder,delta=delta,dom_freq=dom_freq,noise_wavefile=noise_wavefile,vad = vad)
#transpose the features to go from left to right in time:
feats = np.transpose(feats)
#create graph and save to png
plt.clf()
librosa.display.specshow(feats)
if noise_wavefile:
noise = True
else:
noise = False
plt.title("{}: {} timesteps, frame width of {}".format(wav,time_step,frame_width))
plt.tight_layout(pad=0)
pic_path = "{}{}_vad{}_noise{}_delta{}_domfreq{}".format(feature_type,num_feature_columns,vad,noise,delta,dom_freq)
path = unique_path(Path(head_folder), pic_path+"{:03d}.png")
plt.savefig(path)
return True
开发者ID:a-n-rose,项目名称:Build-CNN-or-LSTM-or-CNNLSTM-with-speech-features,代码行数:22,代码来源:feature_extraction_functions.py
示例4: test_melfilter_librosa
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def test_melfilter_librosa():
filename = librosa.util.example_audio_file()
y, sr = librosa.load(filename, offset=1.0, duration=0.3)
n_fft = 1024
hop_length = 256
fmin = 500
fmax = 5000
n_mels = 16
spec = numpy.abs(librosa.core.stft(y, n_fft=n_fft, hop_length=hop_length))**2
spec1 = spec[:,0]
ref = librosa.feature.melspectrogram(S=spec1, sr=sr, norm=None, htk=True, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
out = eml_audio.melfilter(spec1, sr, n_fft, n_mels, fmin, fmax)
fig, (ref_ax, out_ax) = plt.subplots(2)
def specshow(d, ax):
s = librosa.amplitude_to_db(d, ref=numpy.max)
librosa.display.specshow(s, ax=ax, x_axis='time')
specshow(ref.reshape(-1, 1), ax=ref_ax)
specshow(out.reshape(-1, 1), ax=out_ax)
fig.savefig('melfilter.librosa.png')
assert ref.shape == out.shape
numpy.testing.assert_allclose(ref, out, rtol=0.01)
示例5: plotTempogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def plotTempogram(self):
"""
The tempogram visualizes the rhythm (pattern recurrence), using the
onset envelope, oenv, to determine the start points for the patterns.
"""
oenv = librosa.onset.onset_strength(y=self.wav, sr=self.samplefreq, hop_length=512)
tempogram = librosa.feature.tempogram(onset_envelope=oenv, sr=self.samplefreq, hop_length=512)
librosa.display.specshow(tempogram, sr=self.samplefreq, hop_length=512, x_axis='time', y_axis='tempo')
plt.colorbar()
plt.title('Tempogram')
plt.tight_layout()
plt.show()
plt.plot(oenv, label='Onset strength')
plt.title('Onset Strength Over Time')
plt.xlabel('Time')
plt.ylabel('Onset Strength')
plt.show()
return tempogram
开发者ID:nlinc1905,项目名称:Convolutional-Autoencoder-Music-Similarity,代码行数:20,代码来源:02_wav_features_and_spectrogram.py
示例6: visualization_tensor_spectrogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def visualization_tensor_spectrogram(mel_spectrogram, title):
"""visualizing first one result of SpecAugment
# Arguments:
mel_spectrogram(ndarray): mel_spectrogram to visualize.
title(String): plot figure's title
"""
# session for plotting
sess = tf.InteractiveSession()
mel_spectrogram = mel_spectrogram.eval()
# Show mel-spectrogram using librosa's specshow.
plt.figure(figsize=(10, 4))
librosa.display.specshow(librosa.power_to_db(mel_spectrogram[0, :, :, 0], ref=np.max), y_axis='mel', fmax=8000,
x_axis='time')
# plt.colorbar(format='%+2.0f dB')
plt.title(title)
plt.tight_layout()
plt.show()
示例7: draw_lines
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def draw_lines(num_samples, sample_rate, lines):
"""Debugging function to draw detected lines in black"""
lines_matrix = np.zeros((num_samples, num_samples))
for line in lines:
lines_matrix[line.lag:line.lag + 4, line.start:line.end + 1] = 1
# Import here since this function is only for debugging
import librosa.display
import matplotlib.pyplot as plt
librosa.display.specshow(
lines_matrix,
y_axis='time',
x_axis='time',
sr=sample_rate / (N_FFT / 2048))
plt.colorbar()
plt.set_cmap("hot_r")
plt.show()
示例8: _create_plot
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def _create_plot(spectrogram,
sr,
nfft,
ylim=None,
cmap='viridis',
scale='linear',
**kwargs):
if not ylim:
ylim = sr / 2
spectrogram_axes = librosa.display.specshow(spectrogram,
hop_length=int(nfft / 2),
fmax=ylim,
sr=sr,
cmap=cmap,
y_axis=scale,
x_axis='time')
if scale == 'linear':
spectrogram_axes.set_ylim(0, ylim)
return spectrogram_axes
示例9: plot_wave
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def plot_wave(sound_files, sound_names):
"""plot wave"""
i = 1
fig = plt.figure(figsize=(20, 64))
for f, n in zip(sound_files, sound_names):
y, sr = librosa.load(os.path.join('./data/esc10/audio/', f))
plt.subplot(10, 1, i)
librosa.display.waveplot(y, sr, x_axis=None)
plt.title(n + ' - ' + 'Wave')
i += 1
plt.tight_layout(pad=10)
plt.show()
示例10: plot_spectrum
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def plot_spectrum(sound_files, sound_names):
"""plot log power spectrum"""
i = 1
fig = plt.figure(figsize=(20, 64))
for f, n in zip(sound_files, sound_names):
y, sr = librosa.load(os.path.join('./data/esc10/audio/', f))
plt.subplot(10, 1, i)
D = librosa.logamplitude(np.abs(librosa.stft(y)) ** 2, ref_power=np.max)
librosa.display.specshow(D, sr=sr, y_axis='log')
plt.title(n + ' - ' + 'Spectrum')
i += 1
plt.tight_layout(pad=10)
plt.show()
示例11: show_melspectrogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def show_melspectrogram(mels, title='Log-frequency power spectrogram'):
import matplotlib.pyplot as plt
librosa.display.specshow(mels, x_axis='time', y_axis='mel',
sr=config.sampling_rate, hop_length=config.hop_length,
fmin=config.fmin, fmax=config.fmax)
plt.colorbar(format='%+2.0f dB')
plt.title(title)
plt.show()
示例12: read_as_melspectrogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def read_as_melspectrogram(file_path, time_stretch=1.0, pitch_shift=0.0,
debug_display=False):
x = read_audio(file_path)
if time_stretch != 1.0:
x = librosa.effects.time_stretch(x, time_stretch)
if pitch_shift != 0.0:
librosa.effects.pitch_shift(x, config.sampling_rate, n_steps=pitch_shift)
mels = audio_to_melspectrogram(x)
if debug_display:
import IPython
IPython.display.display(IPython.display.Audio(x, rate=config.sampling_rate))
show_melspectrogram(mels)
return mels
示例13: plot_mfcc
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def plot_mfcc(mfcc):
plt.figure(figsize=(10, 4))
librosa.display.specshow(mfcc, x_axis='time')
plt.colorbar()
plt.title('MFCC')
plt.tight_layout()
plt.show()
示例14: visualization_spectrogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def visualization_spectrogram(mel_spectrogram, title):
"""visualizing first one result of SpecAugment
# Arguments:
mel_spectrogram(ndarray): mel_spectrogram to visualize.
title(String): plot figure's title
"""
# Show mel-spectrogram using librosa's specshow.
plt.figure(figsize=(10, 4))
librosa.display.specshow(librosa.power_to_db(mel_spectrogram[0, :, :, 0], ref=np.max), y_axis='mel', fmax=8000, x_axis='time')
plt.title(title)
plt.tight_layout()
plt.show()
示例15: visualization_tensor_spectrogram
# 需要导入模块: import librosa [as 别名]
# 或者: from librosa import display [as 别名]
def visualization_tensor_spectrogram(mel_spectrogram, title):
"""visualizing first one result of SpecAugment
# Arguments:
mel_spectrogram(ndarray): mel_spectrogram to visualize.
title(String): plot figure's title
"""
# Show mel-spectrogram using librosa's specshow.
plt.figure(figsize=(10, 4))
librosa.display.specshow(librosa.power_to_db(mel_spectrogram[0, :, :, 0], ref=np.max), y_axis='mel', fmax=8000, x_axis='time')
# plt.colorbar(format='%+2.0f dB')
plt.title(title)
plt.tight_layout()
plt.show()