danstowell

#!/bin/env python

# Script to enforce a maximum duration on a set of audio files.
# Long files are trimmed, without altering their file format.
# Uses ffmpeg. (So, that needs to be installed on your system).
# Tested in Ubuntu Linux v22.04.

# Written by Dan Stowell 2024.
# CC0: This work has been marked as dedicated to the public domain.

danstowell

import os
import sys

import imageio
import numpy as np
import librosa as lr
import matplotlib.pyplot as plt
from tqdm import tqdm

danstowell

# example of bootstrap sampling to estimate confidence intervals on an accuracy measure

import numpy as np


nbootstraps   = 500 # 50 # 500    # note that 50 is fast enough for development purposes, but I use 500 for final evaluation

# here's a VERY SHORT list of outcomes, each one reflecting whether sound X was correctly predicted or not
outcomes = np.array([1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0])

danstowell

import os, sys, csv
import numpy as np
import pandas as pd

from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
import imageio

###################################################################################

danstowell

# simple audio sync example by Dan Stowell Nov 2018

import librosa  # lib... Rosa!

import os
import numpy as np

###############################################

maxlagsecs = 10   # the maximum offset between two audio files that will be considered

danstowell

# by Dan Stowell 2014.
# This particular file implementing ReservoirSampleMatrix is published under the MIT Public Licence
# http://opensource.org/licenses/MIT

import numpy as np
import random

class ReservoirSampleMatrix:
	"""This class helps you to take a uniform random sample from streaming data, using the 'Reservoir Sampling' technique.

danstowell

import numpy as np
from numpy import log

# we generate two independent "parent" processes, then for each one we independently create thinnings.
# we aim to have a distance measure that correctly clusters the two sets according to their parent.
# by Dan Stowell, copyright Feb 2018

parents = [np.cumsum(np.random.exponential(size=100)) for whichp in range(2)]

danstowell

danstowell

import os, sys, re
from datetime import datetime
from imposm.parser import OSMParser

########################################################

#osmsourcelbl = 'greater-london'
#osmsourcelbl = 'great-britain'
osmsourcelbl = 'planet'

danstowell

// schroeder-phase waveforms

s.boot
s.scope

// we'll do the simple flat-spectra case
// (The paper I saw this version in is "Phase effects on the perceived elevation of complex tones", <http://dx.doi.org/10.1121/1.3372753>.)

~nb = 5;
~nt = 250;