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Merged
cclauss merged 19 commits into from
Sep 24, 2023
Merged

Add MFCC Feature Extraction Algorithm #9057

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264bc13
Add MFCC feature extraction to machine learning
AmirLavasani Sep 12, 2023
a1cb36c
Add standalone usage in comments
AmirLavasani Sep 12, 2023
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Apply suggestions from code review
AmirLavasani Sep 17, 2023
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Delete empty junk file (#9062)
tianyizheng02 Sep 16, 2023
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[main] Fix typo due to auto review change
AmirLavasani Sep 17, 2023
1d843b5
Add doctests for all functions
AmirLavasani Sep 17, 2023
d14cc09
Add MFCC feature extraction to machine learning
AmirLavasani Sep 12, 2023
088eaee
Add standalone usage in comments
AmirLavasani Sep 12, 2023
554d52b
Apply suggestions from code review
AmirLavasani Sep 17, 2023
f78fd1b
[main] Fix typo due to auto review change
AmirLavasani Sep 17, 2023
cb9d9df
Add doctests for all functions
AmirLavasani Sep 17, 2023
d6bdf63
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Sep 17, 2023
c960a23
Merge branch to resolve conflict
AmirLavasani Sep 17, 2023
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Fix some pre-commit issues
AmirLavasani Sep 17, 2023
2fde552
Update review issues
AmirLavasani Sep 17, 2023
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cclauss Sep 24, 2023
7e38bc3
Update mfcc.py
cclauss Sep 24, 2023
f410bf2
get_filter_points() -> tuple[np.ndarray, np.ndarray]:
cclauss Sep 24, 2023
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algorithm
cclauss Sep 24, 2023
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@cclauss
cclauss authored Sep 24, 2023
commit ad0eeefd6df76d26ce537e5393b383ea2b550ac4
4 changes: 0 additions & 4 deletions machine_learning/mfcc.py
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Expand Up @@ -93,7 +93,6 @@ def mfcc(
ValueError: If the input audio is empty.

Example:
>>> import numpy as np
>>> sample_rate = 44100 # Sample rate of 44.1 kHz
>>> duration = 2.0 # Duration of 1 second
>>> t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
Expand Down Expand Up @@ -195,7 +194,6 @@ def audio_frames(
An array of overlapping frames.

Examples:
>>> import numpy as np
>>> audio = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]*1000)
>>> sample_rate = 8000
>>> frames = audio_frames(audio, sample_rate, hop_length=10, ftt_size=512)
Expand Down Expand Up @@ -233,7 +231,6 @@ def calculate_fft(audio_windowed: np.ndarray, ftt_size: int = 1024) -> np.ndarra
The FFT of the audio data.

Examples:
>>> import numpy as np
>>> audio_windowed = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
>>> audio_fft = calculate_fft(audio_windowed, ftt_size=4)
>>> np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j, -1.5-0.8660254j]))
Expand Down Expand Up @@ -268,7 +265,6 @@ def calculate_signal_power(audio_fft: np.ndarray) -> np.ndarray:
The power of the audio signal.

Examples:
>>> import numpy as np
>>> audio_fft = np.array([1+2j, 2+3j, 3+4j, 4+5j])
>>> power = calculate_signal_power(audio_fft)
>>> np.allclose(power, np.array([5, 13, 25, 41]))
Expand Down