carlthome

fedden

jesseengel

import os

import librosa
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['svg.fonttype'] = 'none'
import numpy as np
from scipy.io.wavfile import read as readwav

# Constants

kastnerkyle

raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest")

# License: BSD 3-clause
# Authors: Kyle Kastner
# Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise
# http://ml.cs.yamanashi.ac.jp/world/english/
# MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl
# Pieces also adapted from SPTK
from __future__ import division
import numpy as np

mattpodwysocki

function RxBus() {

  function identity() { }

  function remove(xs, x) {
     xs.splice(xs.indexOf(x), 1);
  }

  function innerSubscription(observable) {
    var disposable;

endolith

Adaptation of Sethares' dissonance measurement function to Python

Example is meant to match the curve in Figure 3:

Original model used products of the two amplitudes a1⋅a2, but this was changed to minimum of the two amplitudes min(a1, a2), as explained in G: Analysis of the Time Domain Model appendix of Tuning, Timbre, Spectrum, Scale.

This weighting is incorporated into the dissonance model (E.2) by assuming that the roughness is proportional to the loudness of the beating. ... Thus, the amplitude of the beating is given by the minimum of the two amplitudes.

endolith

# http://en.wikipedia.org/wiki/Spectral_centroid

from __future__ import division
from numpy import abs, sum, linspace
from numpy.fft import rfft

spectrum = abs(rfft(signal))
normalized_spectrum = spectrum / sum(spectrum)  # like a probability mass function
normalized_frequencies = linspace(0, 1, len(spectrum))
spectral_centroid = sum(normalized_frequencies * normalized_spectrum)