CODEWITHSUNDEEP
pythonaudiosignal-processingspectrogramlibrosa
Ramon Griffo
Ramon Griffo

Reputation: 343

Can I convert spectrograms generated with librosa back to audio?

I converted some audio files to spectrograms and saved them to files using the following code:

import os
from matplotlib import pyplot as plt
import librosa
import librosa.display
import IPython.display as ipd

audio_fpath = "./audios/"
spectrograms_path = "./spectrograms/"
audio_clips = os.listdir(audio_fpath)

def generate_spectrogram(x, sr, save_name):
    X = librosa.stft(x)
    Xdb = librosa.amplitude_to_db(abs(X))
    fig = plt.figure(figsize=(20, 20), dpi=1000, frameon=False)
    ax = fig.add_axes([0, 0, 1, 1], frameon=False)
    ax.axis('off')
    librosa.display.specshow(Xdb, sr=sr, cmap='gray', x_axis='time', y_axis='hz')
    plt.savefig(save_name, quality=100, bbox_inches=0, pad_inches=0)
    librosa.cache.clear()

for i in audio_clips:
    audio_fpath = "./audios/"
    spectrograms_path = "./spectrograms/"
    audio_length = librosa.get_duration(filename=audio_fpath + i)
    j=60
    while j < audio_length:
        x, sr = librosa.load(audio_fpath + i, offset=j-60, duration=60)
        save_name = spectrograms_path + i + str(j) + ".jpg"
        generate_spectrogram(x, sr, save_name)
        j += 60
        if j >= audio_length:
            j = audio_length
            x, sr = librosa.load(audio_fpath + i, offset=j-60, duration=60)
            save_name = spectrograms_path + i + str(j) + ".jpg"
            generate_spectrogram(x, sr, save_name)

I wanted to keep the most detail and quality from the audios, so that i could turn them back to audio without too much loss (They are 80MB each).

Is it possible to turn them back to audio files? How can I do it?

Example spectrograms

I tried using librosa.feature.inverse.mel_to_audio, but it didn't work, and I don't think it applies.

I now have 1300 spectrogram files and want to train a Generative Adversarial Network with them, so that I can generate new audios, but I don't want to do it if i wont be able to listen to the results later.

Upvotes: 7

Views: 4997

Answers (2)

martinwguy
martinwguy

Reputation: 972

I did this ex-novo in 2016 to recover audio from spectrograms for which no audio was available. I didn't know about the GLA (thanks!) but the algorithm sounds similar, complete with random phases.

As regards importing the spectrograms, for mine you indicate the corners of the graphic and its pixels-per-second and frequency range, and the start and end points of the scale and its range, and a script does the color-to-dB mapping of the graph.

Code: https://gitlab.com/martinwguy/delia-derbyshire/-/tree/master/anal Examples of its output: https://wikidelia.net/wiki/Spectrograms#Inverse_spectrograms

Upvotes: 0

Lukasz Tracewski
Lukasz Tracewski

Reputation: 11377

Yes, it is possible to recover most of the signal and estimate the phase with e.g. Griffin-Lim Algorithm (GLA). Its "fast" implementation for Python can be found in librosa. Here's how you can use it:

import numpy as np
import librosa

y, sr = librosa.load(librosa.util.example_audio_file(), duration=10)
S = np.abs(librosa.stft(y))
y_inv = librosa.griffinlim(S)

And that's how the original and reconstruction look like:

reconstruction

The algorithm by default randomly initialises the phases and then iterates forward and inverse STFT operations to estimate the phases.

Looking at your code, to reconstruct the signal, you'd just need to do:

import numpy as np

X_inv = librosa.griffinlim(np.abs(X))

It's just an example of course. As pointed out by @PaulR, in your case you'd need to load the data from jpeg (which is lossy!) and then apply inverse transform to amplitude_to_db first.

The algorithm, especially the phase estimation, can be further improved thanks to advances in artificial neural networks. Here is one paper that discusses some enhancements.

Upvotes: 13

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