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docs: Fix a few typos #375

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docs: Fix a few typos #375

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4 changes: 2 additions & 2 deletions pyAudioAnalysis/ShortTermFeatures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -332,7 +332,7 @@ def chromagram(signal, sampling_rate, window, step, plot=False,
sampling_rate: the sampling freq (in Hz)
window: the short-term window size (in samples)
step: the short-term window step (in samples)
plot: flag, 1 if results are to be ploted
plot: flag, 1 if results are to be plotted
RETURNS:
"""
window = int(window)
Expand Down Expand Up @@ -397,7 +397,7 @@ def spectrogram(signal, sampling_rate, window, step, plot=False,
sampling_rate: the sampling freq (in Hz)
window: the short-term window size (in samples)
step: the short-term window step (in samples)
plot: flag, 1 if results are to be ploted
plot: flag, 1 if results are to be plotted
show_progress flag for showing progress using tqdm
RETURNS:
"""
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6 changes: 3 additions & 3 deletions pyAudioAnalysis/audioSegmentation.py
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Expand Up @@ -682,10 +682,10 @@ def silence_removal(signal, sampling_rate, st_win, st_step, smooth_window=0.5,
- signal: the input audio signal
- sampling_rate: sampling freq
- st_win, st_step: window size and step in seconds
- smoothWindow: (optinal) smooth window (in seconds)
- weight: (optinal) weight factor (0 < weight < 1)
- smoothWindow: (optional) smooth window (in seconds)
- weight: (optional) weight factor (0 < weight < 1)
the higher, the more strict
- plot: (optinal) True if results are to be plotted
- plot: (optional) True if results are to be plotted
RETURNS:
- seg_limits: list of segment limits in seconds (e.g [[0.1, 0.9],
[1.4, 3.0]] means that
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2 changes: 1 addition & 1 deletion pyAudioAnalysis/audioTrainTest.py
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Expand Up @@ -711,7 +711,7 @@ class i is [n_samples x numOfDimensions]
# this is just for debugging (it should be equal to f1)
f1_b = sklearn.metrics.f1_score(y_test_all, y_pred_all,
average='macro')
# Note: np.mean(f1_per_exp) will not be exacty equal to the
# Note: np.mean(f1_per_exp) will not be exactly equal to the
# overall f1 (i.e. f1 and f1_b because these are calculated on a
# per-sample basis)
f1_std = np.std(f1_per_exp)
Expand Down