r9y9

push!(LOAD_PATH, ".")
using BNMF
using PyCall

# bp_nmf のコードと比較するために、librosaを使う
@pyimport librosa

function spectrogram()
    x, ss = librosa.load("test.wav", sr=16000)
    X = abs(librosa.stft(x, n_fft=1024, hop_length=256))

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;; User behaviors
;; -----------------------------
;; Behaviors are stored as a set of diffs that are merged together
;; to create the final set of functionality that makes up Light Table. You can
;; modify these diffs to either add or subtract functionality.
;;
;; Behaviors are added to tags, objects with those tags then automatically gain
;; whatever logic the behavior imparts. To see a list of user-level behaviors,
;; start typing a word related to the functionality you want in between the square
;; brackets (e.g. "theme").

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#!/bin/bash

function install_libsndfile() {
    name=libsndfile-1.0.25.tar.gz
    
    wget -O $name http://www.mega-nerd.com/libsndfile/files/$name
    tar xzvf $name
    
    cd `basename $name ".tar.gz"`
    ./configure --prefix=$PWD/..

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function updateE!(hmm::HMM,
                  Y::AbstractMatrix,     # shape: (D, T)
                  α::Matrix{Float64},   # shape: (K, T)
                  β::Matrix{Float64},   # shape: (K, T)
                  γ::Matrix{Float64},   # shape: (K, T)
                  ξ::Array{Float64, 3}, # shape: (K, K, T-1)
                  B::Matrix{Float64})    # shape: (K, T)
    const D, T = size(Y)

    # Gaussian prob.

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# Hidden Markov Models

using Distributions

type HMM
    K::Int                # number of possible states
    μ::Array{Float64, 2} # shape (D, K)
    π::Array{Float64, 1} # shape (K)
    A::Array{Float64, 2}  # shape (K, K)
    Σ::Array{Float64, 3} # shape (D, D, K)

r9y9

#!/bin/bash

# This is a yet another download script for the cmu arctic speech corpus.
# The corpus will be downloaded in $HOME/data/cmu_arctic/

location=$HOME/data/cmu_arctic/

if [ ! -e $location ]
then
    echo "Create " $location

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backward_plan2{T<:Union(Float32, Float64)}(X::AbstractArray{Complex{T}}, Y::AbstractArray{T}) =
    FFTW.Plan(X, Y, 1, FFTW.ESTIMATE | FFTW.PRESERVE_INPUT, FFTW.NO_TIMELIMIT).plan

function istft2{T<:Union(Float32, Float64)}(S::AbstractMatrix{Complex{T}}, wlen::Int, overlap::Int; nfft=nextfastfft(wlen), window::Union(Function,AbstractVector,Nothing)=nothing)
    winc = wlen-overlap
    win, norm2 = compute_window(window, wlen)
    if win != nothing 
      win² = win.^2
    end
    nframes = size(S,2)-1

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"""
こんな感じでVCできたらいいなぁという設計の例
"""


class Analyizer(object):

    """
    音声分析のインタフェース
    """

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function dft{T}(x::Vector{T})
    N = length(x)
    X = Array(Complex{T}, N)
    for k = 1:N
        s = 0.0 + 0.0im
        for n = 1:N
            s += x[n] * exp(2π * (-im) * k * n / N)
        end
        X[k] = s
    end

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#/bin/bash

# requirement: curl


base=http://hts.sp.nitech.ac.jp/archives/2.3beta/

for name in \
    HTS-demo_NIT-ATR503-M001.tar.bz2 \
    HTS-demo_CMU-ARCTIC-ADAPT.tar.bz2 \