背景
这是电子歌曲中歌曲剪辑的视频。在视频的开头，歌曲全速播放。当您放慢歌曲速度时，您可以听到歌曲使用的所有独特声音。其中一些声音重复。
问题描述
我要做的是创建如下所示的视觉效果，其中为每个独特的声音创建一个水平轨道/行，该轨道上的彩色块对应于声音正在播放的歌曲中的每个时间帧。音轨/行应按声音与每个声音的相似程度排序，更相似的声音靠得更近。如果声音是如此相同以至于人类无法区分它们，那么它们应该被认为是相同的声音。
- 如果它通常可以满足我的要求，我会接受一个不完美的解决方案
- 观看上面链接的视频，了解我所说的视频描述。它包括一个我手动创建的视觉网格，它几乎与我尝试生成的网格相匹配。
例如，如果下面的 5 个波中的每一个都代表一个声音发出的声波，那么这些声音中的每一个都将被认为是相似的，并且将在网格上垂直放置彼此靠近。
尝试
我一直在寻找 librosa 中的拉普拉斯分割示例。标记为结构组件的图形看起来可能是我需要的。从阅读论文来看，他们似乎正试图将歌曲分成合唱、诗句、桥牌等片段……但我实际上是在试图将歌曲分成 1 或 2 个节拍片段。
这是拉普拉斯分割的代码（如果您愿意，也可以使用Jupyter Notebook ）。
# -*- coding: utf-8 -*-
"""
======================
Laplacian segmentation
======================
This notebook implements the laplacian segmentation method of
`McFee and Ellis, 2014 <http://bmcfee.github.io/papers/ismir2014_spectral.pdf>`_,
with a couple of minor stability improvements.
Throughout the example, we will refer to equations in the paper by number, so it will be
helpful to read along.
"""
# Code source: Brian McFee
# License: ISC
###################################
# Imports
# - numpy for basic functionality
# - scipy for graph Laplacian
# - matplotlib for visualization
# - sklearn.cluster for K-Means
#
import numpy as np
import scipy
import matplotlib.pyplot as plt
import sklearn.cluster
import librosa
import librosa.display
import matplotlib.patches as patches
#############################
# First, we'll load in a song
def laplacianSegmentation(fileName):
y, sr = librosa.load(librosa.ex('fishin'))
##############################################
# Next, we'll compute and plot a log-power CQT
BINS_PER_OCTAVE = 12 * 3
N_OCTAVES = 7
C = librosa.amplitude_to_db(np.abs(librosa.cqt(y=y, sr=sr,
bins_per_octave=BINS_PER_OCTAVE,
n_bins=N_OCTAVES * BINS_PER_OCTAVE)),
ref=np.max)
fig, ax = plt.subplots()
librosa.display.specshow(C, y_axis='cqt_hz', sr=sr,
bins_per_octave=BINS_PER_OCTAVE,
x_axis='time', ax=ax)
##########################################################
# To reduce dimensionality, we'll beat-synchronous the CQT
tempo, beats = librosa.beat.beat_track(y=y, sr=sr, trim=False)
Csync = librosa.util.sync(C, beats, aggregate=np.median)
# For plotting purposes, we'll need the timing of the beats
# we fix_frames to include non-beat frames 0 and C.shape[1] (final frame)
beat_times = librosa.frames_to_time(librosa.util.fix_frames(beats,
x_min=0,
x_max=C.shape[1]),
sr=sr)
fig, ax = plt.subplots()
librosa.display.specshow(Csync, bins_per_octave=12*3,
y_axis='cqt_hz', x_axis='time',
x_coords=beat_times, ax=ax)
#####################################################################
# Let's build a weighted recurrence matrix using beat-synchronous CQT
# (Equation 1)
# width=3 prevents links within the same bar
# mode='affinity' here implements S_rep (after Eq. 8)
R = librosa.segment.recurrence_matrix(Csync, width=3, mode='affinity',
sym=True)
# Enhance diagonals with a median filter (Equation 2)
df = librosa.segment.timelag_filter(scipy.ndimage.median_filter)
Rf = df(R, size=(1, 7))
###################################################################
# Now let's build the sequence matrix (S_loc) using mfcc-similarity
#
# :math:`R_\text{path}[i, i\pm 1] = \exp(-\|C_i - C_{i\pm 1}\|^2 / \sigma^2)`
#
# Here, we take :math:`\sigma` to be the median distance between successive beats.
#
mfcc = librosa.feature.mfcc(y=y, sr=sr)
Msync = librosa.util.sync(mfcc, beats)
path_distance = np.sum(np.diff(Msync, axis=1)**2, axis=0)
sigma = np.median(path_distance)
path_sim = np.exp(-path_distance / sigma)
R_path = np.diag(path_sim, k=1) + np.diag(path_sim, k=-1)
##########################################################
# And compute the balanced combination (Equations 6, 7, 9)
deg_path = np.sum(R_path, axis=1)
deg_rec = np.sum(Rf, axis=1)
mu = deg_path.dot(deg_path + deg_rec) / np.sum((deg_path + deg_rec)**2)
A = mu * Rf + (1 - mu) * R_path
###########################################################
# Plot the resulting graphs (Figure 1, left and center)
fig, ax = plt.subplots(ncols=3, sharex=True, sharey=True, figsize=(10, 4))
librosa.display.specshow(Rf, cmap='inferno_r', y_axis='time', x_axis='s',
y_coords=beat_times, x_coords=beat_times, ax=ax[0])
ax[0].set(title='Recurrence similarity')
ax[0].label_outer()
librosa.display.specshow(R_path, cmap='inferno_r', y_axis='time', x_axis='s',
y_coords=beat_times, x_coords=beat_times, ax=ax[1])
ax[1].set(title='Path similarity')
ax[1].label_outer()
librosa.display.specshow(A, cmap='inferno_r', y_axis='time', x_axis='s',
y_coords=beat_times, x_coords=beat_times, ax=ax[2])
ax[2].set(title='Combined graph')
ax[2].label_outer()
#####################################################
# Now let's compute the normalized Laplacian (Eq. 10)
L = scipy.sparse.csgraph.laplacian(A, normed=True)
# and its spectral decomposition
evals, evecs = scipy.linalg.eigh(L)
# We can clean this up further with a median filter.
# This can help smooth over small discontinuities
evecs = scipy.ndimage.median_filter(evecs, size=(9, 1))
# cumulative normalization is needed for symmetric normalize laplacian eigenvectors
Cnorm = np.cumsum(evecs**2, axis=1)**0.5
# If we want k clusters, use the first k normalized eigenvectors.
# Fun exercise: see how the segmentation changes as you vary k
k = 5
X = evecs[:, :k] / Cnorm[:, k-1:k]
# Plot the resulting representation (Figure 1, center and right)
fig, ax = plt.subplots(ncols=2, sharey=True, figsize=(10, 5))
librosa.display.specshow(Rf, cmap='inferno_r', y_axis='time', x_axis='time',
y_coords=beat_times, x_coords=beat_times, ax=ax[1])
ax[1].set(title='Recurrence similarity')
ax[1].label_outer()
librosa.display.specshow(X,
y_axis='time',
y_coords=beat_times, ax=ax[0])
ax[0].set(title='Structure components')
#############################################################
# Let's use these k components to cluster beats into segments
# (Algorithm 1)
KM = sklearn.cluster.KMeans(n_clusters=k)
seg_ids = KM.fit_predict(X)
# and plot the results
fig, ax = plt.subplots(ncols=3, sharey=True, figsize=(10, 4))
colors = plt.get_cmap('Paired', k)
librosa.display.specshow(Rf, cmap='inferno_r', y_axis='time',
y_coords=beat_times, ax=ax[1])
ax[1].set(title='Recurrence matrix')
ax[1].label_outer()
librosa.display.specshow(X,
y_axis='time',
y_coords=beat_times, ax=ax[0])
ax[0].set(title='Structure components')
img = librosa.display.specshow(np.atleast_2d(seg_ids).T, cmap=colors,
y_axis='time', y_coords=beat_times, ax=ax[2])
ax[2].set(title='Estimated segments')
ax[2].label_outer()
fig.colorbar(img, ax=[ax[2]], ticks=range(k))
###############################################################
# Locate segment boundaries from the label sequence
bound_beats = 1 + np.flatnonzero(seg_ids[:-1] != seg_ids[1:])
# Count beat 0 as a boundary
bound_beats = librosa.util.fix_frames(bound_beats, x_min=0)
# Compute the segment label for each boundary
bound_segs = list(seg_ids[bound_beats])
# Convert beat indices to frames
bound_frames = beats[bound_beats]
# Make sure we cover to the end of the track
bound_frames = librosa.util.fix_frames(bound_frames,
x_min=None,
x_max=C.shape[1]-1)
###################################################
# And plot the final segmentation over original CQT
# sphinx_gallery_thumbnail_number = 5
bound_times = librosa.frames_to_time(bound_frames)
freqs = librosa.cqt_frequencies(n_bins=C.shape[0],
fmin=librosa.note_to_hz('C1'),
bins_per_octave=BINS_PER_OCTAVE)
fig, ax = plt.subplots()
librosa.display.specshow(C, y_axis='cqt_hz', sr=sr,
bins_per_octave=BINS_PER_OCTAVE,
x_axis='time', ax=ax)
for interval, label in zip(zip(bound_times, bound_times[1:]), bound_segs):
ax.add_patch(patches.Rectangle((interval[0], freqs[0]),
interval[1] - interval[0],
freqs[-1],
facecolor=colors(label),
alpha=0.50))我认为必须改变的一件主要事情是集群的数量，在示例中它们有 5 个，但我不知道我想要它是什么，因为我不知道有多少声音。我将它设置为 400 产生以下结果，这并不是我可以使用的东西。理想情况下，我希望所有块都是纯色：而不是介于最大红色和蓝色值之间的颜色。
（我把它横着看更像我上面的例子，更像我试图产生的输出）
附加信息
背景中可能还有鼓音轨，有时会同时播放多个声音。如果这些多个声音组被解释为一个独特的声音，那没关系，但我显然更喜欢它们是否可以被区分为单独的声音。
如果它更容易，您可以使用删除鼓循环
y, sr = librosa.load(librosa.ex('exampleSong.mp3'))
y_harmonic, y_percussive = librosa.effects.hpss(y)
更新
我能够通过瞬变分离声音。目前这种作品，但是它分成了太多的声音，据我所知，它似乎主要只是将一些声音分成两个。我还可以从我正在使用的软件创建一个 midi 文件，并使用它来确定瞬态时间，但如果可以的话，我想在没有 midi 文件的情况下解决这个问题。midi 文件非常准确，将声音文件分成 33 个部分，而瞬态代码将声音文件分成 40 个部分。这是midi的可视化
所以仍然需要解决的部分将是
- 更好的瞬态分离
- 对声音进行排序
