find_delay documentation
What is find_delay?
find_delay is a Python package that tries to find the delay where a time series appear in another via cross-correlation. It can theoretically work with any time series, but was created to try to align audio files.
How do I install find_delay?
You can install find_delay using:
python3 -m pip install find_delay
py -m pip install find_delay
How do I use find_delay?
To find when an excerpt starts in an array, use the find_delay function and fill only the first five parameters:
array_1
: The array that contains the excerpt
array_2
: The excerpt
freq_array_1
: The frequency of the array (if it’s important, otherwise set it at 1)
freq_array_2
: The frequency of the excerpt (it it’s important, otherwise set it at 1)
compute_envelope
: If you are trying to find the delay between two audio files, set on True, otherwise False.
Leave the other parameters default (just set plot_figure = True
if you want to visualize the output of the
function). This should be enough for most cases, but you can fine tune other parameters of which you can find the
description here.
If you need to find multiple excerpts into one big array, call find_delays instead.
Examples
Example 1: two arrays of random numbers
array_1 = [24, 70, 28, 59, 13, 97, 63, 30, 89, 4, 8, 15, 16, 23, 42, 37, 70, 18, 59, 48, 41, 83, 99, 6, 24, 86]
array_2 = [4, 8, 15, 16, 23, 42]
find_delay(array_1, array_2, 1, 1, False, path_figure="figure_1.png")

Example 2: sine function, different frequencies
timestamps_1 = np.linspace(0, np.pi * 2, 200001)
array_1 = np.sin(timestamps_1)
timestamps_2 = np.linspace(np.pi * 0.5, np.pi * 0.75, 6001)
array_2 = np.sin(timestamps_2)
find_delay(array_1, array_2, 100000 / np.pi, 6000 / (np.pi / 4),
compute_envelope=False, resampling_rate=1000, window_size_res=20000, overlap_ratio_res=0.5,
resampling_mode="cubic", plot_figure=True, path_figure="figure_2.png",
plot_intermediate_steps=True, verbosity=1)

Example 3: audio files, different frequencies
audio_path = "i_have_a_dream_full_speech.wav"
audio_wav = wavfile.read(audio_path)
audio_frequency = audio_wav[0]
audio_array = audio_wav[1][:, 0] # Turn to mono
excerpt_path = "i_have_a_dream_excerpt.wav"
excerpt_wav = wavfile.read(excerpt_path)
excerpt_frequency = excerpt_wav[0]
excerpt_array = excerpt_wav[1][:, 0] # Turn to mono
find_delay(audio_array, excerpt_array, audio_frequency, excerpt_frequency,
compute_envelope=True, window_size_env=1e6, overlap_ratio_env=0.5,
resampling_rate=1000, window_size_res=1e7, overlap_ratio_res=0.5, return_delay_format="timedelta",
resampling_mode="cubic", plot_figure=True, path_figure="figure_3.png",
plot_intermediate_steps=True, verbosity=1)
