如何将Librosa谱图图另存为特定大小的图像?

问题描述

因此，我想将声谱图图像馈送到卷积神经网络，以尝试对各种声音进行分类.我希望每个图像都精确地为384x128像素.但是，当我实际保存图像时，它仅为297x98.这是我的代码:

So I'm wanting to feed spectrogram images to a convolutional neural network as an attempt to classify various sounds. I want each image to be exactly 384x128 pixels. However, when I actually save the image it is only 297x98. Here's my code:

def save_spectrogram(num):
  dpi = 128
  x_pixels = 384
  y_pixels = 128
  samples, sr = load_wave(num)
  stft = np.absolute(librosa.stft(samples))
  db = librosa.amplitude_to_db(stft, ref=np.max)
  fig = plt.figure(figsize=(x_pixels//dpi, y_pixels//dpi), dpi=dpi, frameon=False)
  ax = fig.add_subplot(111)
  ax.axes.get_xaxis().set_visible(False)
  ax.axes.get_yaxis().set_visible(False)
  ax.set_frame_on(False)
  librosa.display.specshow(db, y_axis='linear')
  plt.savefig(TRAIN_IMG+str(num)+'.jpg', bbox_inches='tight', pad_inches=0, dpi=dpi)

有人对我如何解决此问题有任何指示吗?我也尝试过在没有子图的情况下执行此操作，但是当我执行该操作时，它仍会保存为错误的大小并具有空白/背景.

Does anyone have any pointers on how I can fix this? I've also tried doing it without the subplot, but when I do that it still saves as the wrong size AND has white space/background.

推荐答案

图形供人类查看，其中包含诸如轴标记，标签等对机器学习无用的内容.要用光谱图的图像"填充模型，应仅输出数据.此数据可以任何格式存储，但是如果要使用标准图像格式，则应使用PNG.JPEG之类的有损压缩会引入压缩伪像.

Plots are for humans to look at, and contains things like axis markers, labels etc that are not useful for machine learning. To feed a model with an 'image' of the spectrogram, one should output only the data. This data be stored in any format, but if you want to use a standard image format then should use PNG. Lossy compression such as JPEG introduces compression artifacts.

此处遵循工作示例代码以保存频谱图.请注意，要获得固定大小的图像输出，代码将提取音频信号的固定长度窗口.将音频流划分为固定长度的分析窗口是标准做法.

Here follows working example code to save spectrogram. Note that to get a fixed size image output, the code extracts a fixed-length window of the audio signal. Dividing an audio stream into such fixed-length analysis windows is standard practice.

import librosa
import numpy
import skimage.io

def scale_minmax(X, min=0.0, max=1.0):
    X_std = (X - X.min()) / (X.max() - X.min())
    X_scaled = X_std * (max - min) + min
    return X_scaled

def spectrogram_image(y, sr, out, hop_length, n_mels):
    # use log-melspectrogram
    mels = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=n_mels,
                                            n_fft=hop_length*2, hop_length=hop_length)
    mels = numpy.log(mels + 1e-9) # add small number to avoid log(0)

    # min-max scale to fit inside 8-bit range
    img = scale_minmax(mels, 0, 255).astype(numpy.uint8)
    img = numpy.flip(img, axis=0) # put low frequencies at the bottom in image
    img = 255-img # invert. make black==more energy

    # save as PNG
    skimage.io.imsave(out, img)


if __name__ == '__main__':
    # settings
    hop_length = 512 # number of samples per time-step in spectrogram
    n_mels = 128 # number of bins in spectrogram. Height of image
    time_steps = 384 # number of time-steps. Width of image

    # load audio. Using example from librosa
    path = librosa.util.example_audio_file()
    y, sr = librosa.load(path, offset=1.0, duration=10.0, sr=22050)
    out = 'out.png'

    # extract a fixed length window
    start_sample = 0 # starting at beginning
    length_samples = time_steps*hop_length
    window = y[start_sample:start_sample+length_samples]

    # convert to PNG
    spectrogram_image(window, sr=sr, out=out, hop_length=hop_length, n_mels=n_mels)
    print('wrote file', out)

输出

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