檢視 librosa 版本
import librosa
print(librosa.__version__)y 為訊號向量。sr 為取樣率。y[1000] 表示訊號的第 1001 個樣本。S[:,100] 表示 S 的第 101 幀。
預設引數：sr = 22050，hop_length = 512。
- Low-level audio processes（低階音訊處理）
- Unit conversion（單位換算）
- Time-frequency representations（時頻表示）
要以其原始取樣率家在訊號，使用 sr=None。
To load a signal at its native sampling rate, use sr=None
y_orig, sr_orig = librosa.load(librosa.util.example_audio_file(),
sr=None)
print(len(y_orig), sr_orig)Out: 2710336 44100
Resampling is easy
sr = 22050
y = librosa.resample(y_orig, sr_orig, sr)
print(len(y), sr)Out: 1355168 22050
But what’s that in seconds?
print(librosa.samples_to_time(len(y), sr))Out: 61 .45886621315193
Spectral representations
Short-time Fourier transform underlies most analysis.
短時傅立葉變換是大多數分析的基礎。librosa.stft returns a complex matrix D.librosa.stft 返回一個複數矩陣 D。D[f, t] is the FFT value at frequency f, time (frame) t.D[f, t] 是在頻率 f，時間（幀）處的 FFT 值 t。
D = librosa.stft(y)
print(D.shape, D.dtype)Out: (1025, 2647) complex64
Often, we only care about the magnitude.
通常，我們只關心幅度。D contains both magnitude S and phase ?.D 包含幅度 S 和相位 ?。
D_{ft}=S_{ft}\exp(j\phi_{ft})
import numpy as np
S, phase = librosa.magphase(D)
print(S.dtype, phase.dtype, np.allclose(D, S * phase))Out: float32 complex64 True
Constant-Q transforms
The CQT gives a logarithmically spaced frequency basis.
CQT提供了對數間隔的頻率基礎。
This representation is more natural for many analysis tasks.
對於許多分析任務而言，這種表示更為自然。
C = librosa.cqt(y, sr=sr)
print(C.shape, C.dtype)Out: (84, 2647) complex128
Exercise 0
- Load a different audio file
- Compute its STFT with a different hop length
# Exercise 0 solution
y2, sr2 = librosa.load( )
D = librosa.stft(y2, hop_length= )- Standard features(標準功能):
librosa.feature.melspectrogramlibrosa.feature.mfcclibrosa.feature.chroma- Lots more…
- Feature manipulation(功能操縱):
librosa.feature.stack_memorylibrosa.feature.delta
大多數功能都可與音訊或 STFT 輸入配合使用
Most features work either with audio or STFT input
melspec = librosa.feature.melspectrogram(y=y, sr=sr)
# Melspec assumes power, not energy as input
# 假定功率作為輸入, 而非能量
melspec_stft = librosa.feature.melspectrogram(S=S**2, sr=sr)
print(np.allclose(melspec, melspec_stft))- Plotting routines for spectra and waveforms
頻譜和波形的繪圖例程 - Note: major overhaul coming in 0.5
# Displays are built with matplotlib
import matplotlib.pyplot as plt
# Let's make plots pretty
import matplotlib.style as ms
ms.use('seaborn-muted')
# Render figures interactively in the notebook
%matplotlib nbagg
# IPython gives us an audio widget for playback
from IPython.display import Audio
import librosa.displayWaveform display
plt.figure()
librosa.display.waveplot(y=y, sr=sr)
A basic spectrogram display
plt.figure()
librosa.display.specshow(melspec, y_axis='mel', x_axis='time')
plt.colorbar()
Exercise 1
Pick a feature extractor from the
librosa.featuresubmodule and plot the output withlibrosa.display.specshowBonus: Customize the plot using either
specshowarguments orpyplotfunctions
# Exercise 1 solution
X = librosa.feature.XX()
plt.figure()
librosa.display.specshow( )- Beat tracking and tempo estimation
節拍跟蹤和速度估計
The beat tracker returns the estimated tempo and beat positions (measured in frames)
節拍跟蹤器返回估計的速度和節拍位置（以幀為單位）
tempo, beats = librosa.beat.beat_track(y=y, sr=sr)
print(tempo)
print(beats)Out: 129 .19921875
[ 5 24 43 63 83 103 122 142 162 182 202 222 242 262
281 301 321 341 361 382 401 421 441 461 480 500 520 540
560 580 600 620 639 658 678 698 718 737 758 777 798 817
837 857 877 896 917 936 957 976 996 1016 1036 1055 1075 1095
1116 1135 1155 1175 1195 1214 1234 1254 1275 1295 1315 1334 1354 1373
1394 1414 1434 1453 1473 1493 1513 1532 1553 1573 1593 1612 1632 1652
1672 1691 1712 1732 1752 1771 1791 1811 1831 1850 1871 1890 1911 1931
1951 1971 1990 2010 2030 2050 2070 2090 2110 2130 2150 2170 2190 2209
2229 2249 2269 2289 2309 2328 2348 2368 2388 2408 2428 2448 2468 2488
2508 2527 2547]
Let’s sonify it!
clicks = librosa.clicks(frames=beats, sr=sr, length=len(y))
Audio(data=y + clicks, rate=sr)
Beats can be used to downsample features
chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
chroma_sync = librosa.feature.sync(chroma, beats)AttributeError: module ‘librosa.feature’ has no attribute ‘sync’
留意下，新版本的 librosa.feature 裡沒有 ‘sync’ 屬性了。
plt.figure(figsize=(6, 3))
plt.subplot(2, 1, 1)
librosa.display.specshow(chroma, y_axis='chroma')
plt.ylabel('Full resolution')
plt.subplot(2, 1, 2)
librosa.display.specshow(chroma_sync, y_axis='chroma')
plt.ylabel('Beat sync')NameError: name ‘chroma_sync’ is not defined
- Self-similarity / recurrence
自相關 / 重現 - Segmentation
分割
Recurrence matrices encode self-similarity
遞迴矩陣編碼自相關
R[i, j] = similarity between frames (i, j)Librosa computes recurrence between k-nearest neighbors.
Librosa 計算 k -nearest 鄰居之間的遞迴。
R = librosa.segment.recurrence_matrix(chroma_sync)
plt.figure(figsize=(4, 4))
librosa.display.specshow(R)We can include affinity weights for each link as well.
我們還可以引入每個連結的關係權重。
R2 = librosa.segment.recurrence_matrix(chroma_sync, mode='affinity', sym=True)
plt.figure(figsize=(5, 4))
librosa.display.specshow(R2)
plt.colorbar()Exercise 2
- Plot a recurrence matrix using different features
- Bonus: Use a custom distance metric
# Exercise 2 solutionhpss: Harmonic-percussive source separationnn_filter: Nearest-neighbor filtering, non-local means, Repet-SIMdecompose: NMF, PCA and friends
Separating harmonics from percussives is easy
將諧波與打擊樂分開很容易
D_harm, D_perc = librosa.decompose.hpss(D)
y_harm = librosa.istft(D_harm)
y_perc = librosa.istft(D_perc)然後可以自己聽一下分開後的音樂
Audio(data=y_harm, rate=sr)
Audio(data=y_perc, rate=sr)NMF is pretty easy also!
# Fit the model
W, H = librosa.decompose.decompose(S, n_components=16, sort=True)plt.figure(figsize=(6, 3))
plt.subplot(1, 2, 1), plt.title('W')
librosa.display.specshow(librosa.logamplitude(W**2), y_axis='log')
plt.subplot(1, 2, 2), plt.title('H')
librosa.display.specshow(H, x_axis='time')AttributeError: module ‘librosa’ has no attribute ‘logamplitude’ 先留意下這個模組變更的問題。
# Reconstruct the signal using only the first component
# 僅使用第一個分量來重建訊號
S_rec = W[:, :1].dot(H[:1, :])
y_rec = librosa.istft(S_rec * phase)
Audio(data=y_rec, rate=sr)Slide Type-SlideSub-SlideFragmentSkipNotes
Exercise 3
- Compute a chromagram using only the harmonic component
僅使用諧波分量計算色譜圖 - Bonus: run the beat tracker using only the percussive component
僅使用打擊樂元件執行節拍跟蹤器
官方文件地址：
This was just a brief intro, but there’s lots more!
Read the docs: librosa.github.io/librosa/
And the example gallery: librosa.github.io/librosa_gallery/
We’ll be sprinting all day. Get involved! github.com/librosa/librosa/issues/...
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