SherazKhan

def extract_feature_means(audio_file_path: str) -> pd.DataFrame:
    # config settings
    number_of_mfcc = c.NUMBER_OF_MFCC

    # 1. Importing 1 file
    y, sr = librosa.load(audio_file_path)

    # Trim leading and trailing silence from an audio signal (silence before and after the actual audio)
    signal, _ = librosa.effects.trim(y)

SherazKhan

function plot_brainstorm_data()
% ============================
% Brainstorm tutorial datasets
% ============================
% 
% Here we compute the evoked from raw for the Brainstorm
% tutorial dataset. For comparison, see [1]_ and:
% 
%     http://neuroimage.usc.edu/brainstorm/Tutorials/MedianNerveCtf
% 

SherazKhan

from scipy.spatial.distance import pdist, squareform
import numpy as np
import random
import copy


def distcorr(Xval, Yval, pval=True, nruns=500):
    """ Compute the distance correlation function, returning the p-value.
    Based on Satra/distcorr.py (gist aa3d19a12b74e9ab7941)

SherazKhan

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""Computes the distance correlation between two matrices.
https://en.wikipedia.org/wiki/Distance_correlation
"""

import numpy as np
from scipy.spatial.distance import pdist, squareform