Independent Component Analysis

Hyvärinen, Aapo, and Erkki Oja. “Independent component analysis: algorithms and applications.” Neural networks 13.4-5 (2000): 411-430.

@article{hyvarinen2000independent,
  title={Independent component analysis: algorithms and applications},
  author={Hyv{\"a}rinen, Aapo and Oja, Erkki},
  journal={Neural networks},
  volume={13},
  number={4-5},
  pages={411--430},
  year={2000},
  publisher={Elsevier}
}

import nussl
import matplotlib.pyplot as plt
import time
import warnings
import numpy as np

warnings.filterwarnings("ignore")
start_time = time.time()

def visualize_and_embed(sources):
    plt.figure(figsize=(10, 6))
    plt.subplot(211)
    nussl.utils.visualize_sources_as_masks(sources,
        y_axis='mel', db_cutoff=-40, alpha_amount=2.0)
    plt.subplot(212)
    nussl.utils.visualize_sources_as_waveform(
        sources, show_legend=False)
    plt.show()
    nussl.play_utils.multitrack(sources)

musdb = nussl.datasets.MUSDB18(download=True)
i = 39

Setting up a signal that ICA can work on

item = musdb[i]
sources = [item['sources']['other'], item['sources']['vocals']]

a = nussl.mixing.pan_audio_signal(sources[0], -35)
a_delays = [np.random.randint(1, 20) for _ in range(a.num_channels)]
a = nussl.mixing.delay_audio_signal(a, a_delays)

b = nussl.mixing.pan_audio_signal(sources[1], 35)
b_delays = [np.random.randint(1, 20) for _ in range(b.num_channels)]
b = nussl.mixing.delay_audio_signal(b, b_delays)

mix = a + b

audio_signals = [
    mix.make_audio_signal_from_channel(ch)
    for ch in range(mix.num_channels)
]

Now running ICA

separator = nussl.separation.factorization.ICA(audio_signals)
estimates = separator()

estimates = {
    f'Source {i}': e for i, e in enumerate(estimates)
}

visualize_and_embed(estimates)

end_time = time.time()
time_taken = end_time - start_time
print(f'Time taken: {time_taken:.4f} seconds')

Time taken: 17.7896 seconds