Librosa简介
Librosa是一个 Python 模块，用于分析一般的音频信号，是一个非常强大的python语音信号处理的第三方库，根据网络资料以及官方教程，本文主要总结了一些重要且常用的功能。
# 安装 !pip install librosa
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import numpy as np import pandas as pd import os import IPython.display as ipd
加载音频文件
import librosa audio_data = 'data/Data_MGTV/angry/audio_1027.wav' x , sr = librosa.load(audio_data) print(x.shape, sr)
(45159,) 22050
x
array([-0.11979394, -0.10811259, -0.04991762, ..., 0.00769441, 0.00752225, 0. ], dtype=float32)
print('x:', x, '\n') print('x shape:', np.shape(x), '\n') print('Sample Rate (KHz):', sr, '\n') print('Check Len of Audio:', np.shape(x)[0]/sr)
x: [-0.11979394 -0.10811259 -0.04991762 ... 0.00769441 0.00752225 0. ] x shape: (45159,) Sample Rate (KHz): 22050 Check Len of Audio: 2.048027210884354
读取时长
d = librosa.get_duration(y=x, sr=22050, S=None, n_fft=2048, hop_length=512, center=True, filename=None) d
2.048027210884354
采样率
sr = librosa.get_samplerate(audio_data) sr
16000
去除两端沉默
audio_file, _ = librosa.effects.trim(x) print('Audio File:', audio_file, '\n') print('Audio File shape:', np.shape(audio_file))
Audio File: [-0.11979394 -0.10811259 -0.04991762 ... 0.00769441 0.00752225 0. ] Audio File shape: (45159,)
播放音频
IPython.display.Audio 可以让我们直接在 jupyter notebook 中播放音频，比如下面包房一段音频
ipd.Audio(audio_data)
波形图
在这里，我们绘制了一个简单的音频波形图。 波图让我们知道给定时间的音频响度。
%matplotlib inline
import sklearn import matplotlib.pyplot as plt import librosa.display plt.figure(figsize=(20, 5)) librosa.display.waveplot(y, sr=sr) plt.show()
Spectogram
频谱图（Spectogram）是声音频率随时间变化的频谱的可视化表示，是给定音频信号的频率随时间变化的表示。'.stft' 将数据转换为短期傅里叶变换。 STFT转换信号，以便我们可以知道给定时间给定频率的幅度。 使用 STFT，我们可以确定音频信号在给定时间播放的各种频率的幅度。
Spectrogram特征是目前在语音识别和环境声音识别中很常用的一个特征，由于CNN在处理图像上展现了强大的能力，使得音频信号的频谱图特征的使用愈加广泛，甚至比MFCC使用的更多。
X = librosa.stft(x) Xdb = librosa.amplitude_to_db(abs(X)) plt.figure(figsize=(20, 5)) librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='hz') plt.colorbar() plt.show()
librosa.display.specshow(Xdb, sr=sr, x_axis='time', y_axis='log') plt.colorbar()
<matplotlib.colorbar.Colorbar at 0x24f53d3e6d8>
梅尔频率倒谱系数（MFCC）
信号的梅尔频率倒谱系数 (MFCC) 是一小组特征（通常约为 10-20），它们简明地描述了频谱包络的整体形状。在 MIR 中，它经常被用来描述音色。
#mcc mfccs = librosa.feature.mfcc(y=x, sr=sr)
mfccs
array([[-1.47507828e+02, -1.39587173e+02, -1.63085953e+02, ..., -3.51147095e+02, -3.62041565e+02, -3.64722260e+02], [ 1.39314545e+02, 1.28688156e+02, 1.26540642e+02, ..., 1.31368317e+02, 1.23287079e+02, 1.06071014e+02], [-5.88899651e+01, -7.76861572e+01, -8.52756119e+01, ..., -3.08440018e+01, -3.50476532e+01, -3.22384949e+01], ..., [ 1.24901953e+01, 2.48859482e+01, 3.59340363e+01, ..., -3.30873656e+00, -5.68462515e+00, -5.88594961e+00], [-6.10755301e+00, -8.72181129e+00, -3.69202137e+00, ..., -2.46745777e+00, -7.76338100e+00, -8.60360718e+00], [-1.22752495e+01, -8.53678513e+00, -2.76085877e+00, ..., 6.47896719e+00, 9.00872326e+00, -3.04730564e-01]], dtype=float32)
mfccs.shape
(20, 89)
在这个例子中，mfcc 在 89 帧中计算了 20 个 MFCC。
第一个 MFCC，第 0 个系数，不传达与频谱整体形状相关的信息。 它只传达一个恒定的偏移量，即向整个频谱添加一个恒定值。 因此，很多情况我们可以在进行分类时会丢弃第一个MFCC。
librosa.display.specshow(mfccs, sr=sr, x_axis='time')
<matplotlib.collections.QuadMesh at 0x24f53d58a20>
过零率
过零率（zero-crossing rate，ZCR）是指一个信号的符号变化的比率，例如信号从正数变成负数，或反过来。这个特征已在语音识别和音乐信息检索领域得到广泛使用，是分类敲击声的关键特征。为真时为1，否则为0。在一些应用场景下，只统计“正向”或“负向”的变化，而不是所有的方向。
n0 = 7000 n1 = 7025 plt.figure(figsize=(14, 5)) plt.plot(x[n0:n1]) plt.show()
zero_crossings = librosa.zero_crossings(x[n0:n1], pad=False)
zero_crossings.shape
(25,)
zero_crossings.sum()
2
可以使用整个音频来遍历这个并推断出整个数据的过零。
zcrs = librosa.feature.zero_crossing_rate(x) print(zcrs.shape)
(1, 89)
plt.figure(figsize=(14, 5)) plt.plot(zcrs[0])
[<matplotlib.lines.Line2D at 0x24f4cd88eb8>]
频谱质心： Spectral Centroid
频谱质心（维基百科）表示频谱能量集中在哪个频率上。这就像一个加权平均值：
其中 S(k) 是频段 k 处的频谱幅度，f(k) 是频段 k 处的频率。
spectral_centroids = librosa.feature.spectral_centroid(x, sr=sr)[0] spectral_centroids.shape
(89,)
frames = range(len(spectral_centroids)) t = librosa.frames_to_time(frames)
import sklearn def normalize(x, axis=0): return sklearn.preprocessing.minmax_scale(x, axis=axis)
librosa.display.waveplot(x, sr=sr, alpha=0.4) plt.plot(t, normalize(spectral_centroids), color='r')
[<matplotlib.lines.Line2D at 0x24f505d4320>]
频谱带宽:Spectral Bandwidth
librosa.feature.spectral_bandwidth 可以用来计算p-order频谱带宽：
其中 S(k) 是频段 k 处的频谱幅度，f(k) 是频段 k 处的频率，fc 是频谱质心。当 p=2 时，这就像一个加权标准差。
spectral_bandwidth_2 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr)[0] spectral_bandwidth_3 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr, p=3)[0] spectral_bandwidth_4 = librosa.feature.spectral_bandwidth(x+0.01, sr=sr, p=4)[0] librosa.display.waveplot(x, sr=sr, alpha=0.4) plt.plot(t, normalize(spectral_bandwidth_2), color='r') plt.plot(t, normalize(spectral_bandwidth_3), color='g') plt.plot(t, normalize(spectral_bandwidth_4), color='y') plt.legend(('p = 2', 'p = 3', 'p = 4'))
<matplotlib.legend.Legend at 0x24f50c08470>
频谱滚降
频谱衰减是总频谱能量的特定百分比所在的频率。
spectral_rolloff = librosa.feature.spectral_rolloff(x+0.01, sr=sr)[0] librosa.display.waveplot(x, sr=sr, alpha=0.4) plt.plot(t, normalize(spectral_rolloff), color='r')
[<matplotlib.lines.Line2D at 0x24f4cc06e10>]
色度特征：Chroma Feature
色度向量 (Wikipedia) 是一个典型的 12 元素特征向量，指示每个音高类别{C, C#, D, D#, E, ..., B} 的能量是多少存在于信号中。
chromagram = librosa.feature.chroma_stft(x, sr=sr, hop_length=512) plt.figure(figsize=(15, 5)) librosa.display.specshow(chromagram, x_axis='time', y_axis='chroma', hop_length=512, cmap='coolwarm')
<matplotlib.collections.QuadMesh at 0x24f4cc9db70>
间距和幅度
音高是声音的感知属性，在与频率相关的尺度上排序，或者更常见的是，音高是可以判断声音在与音乐旋律相关的意义上“更高”和“更低”的质量。
pitches, magnitudes = librosa.piptrack(y=x, sr=sr) print(pitches)
[[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]]
