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Татьяна Фарбер 2025-12-06 02:22:03 +04:00
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39
README.md
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# audio
# Создание снимков аудио файлов через октавное CQT - преобразование
Октавное вейвлет-преобразование для создания эталонных слепков сравнения аудио шумов
Создание частотного-массива (слепка) исходного файла для создания эталонных шаблонов шумов.
## Библиотеки
```
import Pkg; Pkg.add("DSP")
import Pkg; Pkg.add("SignalAnalysis")
import Pkg; Pkg.add("FFTW")
import Pkg; Pkg.add("Plots")
import Pkg; Pkg.add("CSV")
import Pkg; Pkg.add("Tables")
import Pkg; Pkg.add("DataFrames")
import Pkg; Pkg.add("WAV")
```
## Использование
```
julia zaudio.jl test.wav
```
## Шаги
1. Читаем WAV файл
2. Нормализуем уровень средним
3. Разбиваем на кадры
4. Кадры с низкой энергией отбрасываем
5. Осуществляем CQT - преобразование
6. Сохраняем в datasets

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analise.jl Normal file
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using Statistics
using Plots
using Tables
using CSV
using DataFrames
# Папка с датасетами
data_dir = "datasets/"
# Чтение файла данных
function read()
if !isempty(ARGS)
try
data = CSV.read(data_dir*ARGS[1], DataFrame, skipto=2)
ret = []
map(eachrow(data)) do col
push!(ret,std(col))
end
return ret
catch e
println("Ошибка чтения wav-файла: "*e)
end
else
println("Не указан входной файл")
exit()
end
end
# Скользящее среднее
function m_average(arr::Vector{Any}, n::Number)::Vector{Any}
so_far = sum(arr[1:n])
out = zero(arr[n:end])
out[1] = so_far
for (i, (start, stop)) in enumerate(zip(arr, arr[n+1:end]))
so_far += stop - start
out[i+1] = so_far
end
return out
end
audio_data = read()
println(typeof(audio_data))
println(size(audio_data))
println(ndims(audio_data))
println(audio_data[1:41])
audio_data = m_average(audio_data,20)
numbers = size(mel_spectrogram)
minimum_mel = 2595 * log10(1 + (freq / window_length) / 700)
maximum_mel = 2595 * log10(1 + (freq / 2) / 700)
mel_scale = range(minimum_mel, stop = maximum_mel, length = numbers)
hertz_scale = 700 .* (10 .^ (mel_scale / 2595) .- 1)
plot_object = plot(audio_data, xticks = convert(Array{Int}, round.(hertz_scale[1:8:numbers])))
plot!(title = "Sound form", size = (990, 600))
savefig(plot_object, data_dir*ARGS[1]*"-"*"analize.png")

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audio.jl Normal file
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include("./zaf.jl")
using .zaf
using WAV
using Statistics
using Plots
using Tables
using CSV
using DSP
using SignalAnalysis
using DataFrames
using LinearAlgebra
using Logging
global file_frame = ""
global savedata = false #Сохранять ли при обработке спектограммы
detect = true #Флаг необходимости сравнения с эталонами
# Настройки CQT преобразования
octave_resolution = 24
minimum_frequency = 55
maximum_frequency = 9520
time_resolution = 25
# Размеры кадра в сек
window_duration = 0.04;
# Уровень энергии для порогового фильтра
energy_level = 50
# Количество кадров для сглаживания скользящей средней слепка аудио-данных
m_offset = 20
function err(mes)
redirect_stdio(stderr="audio.log") do
logger = ConsoleLogger(stderr)
@error mes
with_logger(logger) do
println(mes)
end
end
exit(2)
end
# Загрузка сохраненного файла данных очищенных окон с преобразованием в среднеквадратичное отклонение по частотным полосам
function load(filename)::Vector{Any}
if !isempty(filename)
try
data = CSV.read(filename, DataFrame, skipto=2)
return data[!, :Column1]
catch e
println("Ошибка чтения csv-файла")
end
else
err("Не указан входной файл")
end
end
function sets()::Dict{String, Vector{Float64}}
datasets = Dict{String, Vector{Float64}}()
for file in readdir("datasets")
datasets[replace(file,".csv" => "")] = load("datasets/"*file)
end
return datasets
end
# Сравнение частотного снимка с сохраненными эталонами
function determ(data)
for (name,frame) in sets()
if cor(data[70:end], frame[70:end]) < 0.99 #Если корреляция по Пирсону по ВЧ низкая, то сигнал невалидный
continue
end
dataLF = normalize(data[1:70])
frame = normalize(frame[1:70])
if sum(broadcast(abs, dataLF-frame)) < 0.2 #Если сумма асболютных отклнений по НЧ низкая, то фрейм совпадает
return name
end
end
return
end
# Скользящее среднее
function m_average(arr::Vector{Float64}, n::Number)::Vector{Any}
so_far = sum(arr[1:n])
out = zero(arr[n:end])
out[1] = so_far
for (i, (start, stop)) in enumerate(zip(arr, arr[n+1:end]))
so_far += stop - start
out[i+1] = so_far
end
return out
end
# Нормализация уровня
function normalizeWAV(audio_input::Matrix{Float64},freq::Number)
return mean(audio_input, dims=2), freq
end
# Разбитие на окна
function windows(freq::Number)
local window_length = nextpow(2, ceil(Int, window_duration*freq))
local window_function = zaf.hamming(window_length, "periodic") # оконная аналитическая функция
return window_length, convert(Int, window_length/2), window_function
end
# Фильтрация окон по уровню минимальной энергии звука в каждом окне
function filter(audio_input::Matrix{Float64},window_length::Number)::Matrix{Float64}
output_signal = zeros(1,1)
number_samples = length(audio_input)
padding_length = floor(Int, window_length / 2)
number_times =
ceil(
Int,
((number_samples + 2 * padding_length) - window_length) /
step_length,
) + 1
local i = 0
for j = 1:(number_times-10) # 10 последних кадров отбарсываем
frame = audio_input[i+1:i+window_length]
if energy(frame) > energy_level
output_signal = [output_signal;frame]
end
i = i + step_length
end
return output_signal
end
# Преобразование Фурье
function stft(audio::Matrix{Float64},freq::Number)
local window_length,step_length,window_function = windows(freq)
return zaf.stft(audio, window_function, step_length), window_length, step_length, window_function
end
# Спектограмма разложения ряда Фурье
function spectrogram(audio::Matrix{Float64},freq::Number)::Matrix{Float64}
local audio_stft, window_length = stft(audio,freq)
local audio_spectrogram = abs.(audio_stft[2:convert(Int, window_length/2)+1, :]) # Отбрасываем постоянную составляющую
local number_samples = length(audio_input)
local xtick_step = 1
local ytick_step = 1000
local plot_object = zaf.specshow(audio_spectrogram, number_samples, freq, xtick_step, ytick_step)
heatmap!(title = "Spectrogram (dB)", size = (990, 600))
savefig(plot_object, file_frame*".spectr.png")
CSV.write(file_frame*".spectr.csv", Tables.table(audio_spectrogram))
return audio_spectrogram
end
# CQT-октавное вевлет-преобразование
function cqt(audio::Matrix{Float64},freq::Number)::Matrix{Float64}
local cqt_kernel = zaf.cqtkernel(freq, octave_resolution, minimum_frequency, maximum_frequency)
cqt_spectrogram = zaf.cqtspectrogram(audio, freq, time_resolution, cqt_kernel)
if savedata
CSV.write(file_frame*".cqt.csv", Tables.table(cqt_spectrogram))
local xtick_step = 1
local plot_object = zaf.cqtspecshow(cqt_spectrogram, time_resolution, octave_resolution, minimum_frequency, xtick_step)
heatmap!(title = "CQT spectrogram (dB)", size = (990, 600))
savefig(plot_object, file_frame*".cqt.png")
end
return cqt_spectrogram
end
# Преобразование вевлет-спектрограммы в спектр среднего значения по октавам со сглаживанием
function raft(spectr::Matrix{Float64})::Vector{Any}
freqs, frames = size(spectr)
raft_data = zeros(freqs)
for i in 1:freqs
raft_data[i] = mean(spectr[i, :])
end
return m_average(raft_data,m_offset)
end
# Cохранение образа записи
function save(audio_data::Vector{Any},freq::Number)::Vector{Any}
CSV.write(file_frame*".csv", Tables.table(audio_data))
numbers = length(audio_data)
xtick_locations = [0:octave_resolution:numbers;]
xtick_labels = convert(
Array{Int},
minimum_frequency * 2 .^ (xtick_locations / octave_resolution),
)
plot_object = plot(audio_data, xticks = (xtick_locations, xtick_labels))
plot!(title = "Sound form", size = (990, 600))
savefig(plot_object, file_frame*".png")
return audio_data
end
# Считываем данные из WAV файла и нормализуем их, частота дискретизации получается из заголовка WAV-файла
if !isempty(ARGS)
global file_frame = replace(ARGS[1],".wav" => "")
if length(ARGS)>1 && ARGS[2] == "-s"
savedata = true
end
if length(ARGS)>1 && ARGS[2] == "-d"
println("Режим формирования нового эталонного датасета")
if ARGS[3] isa String
savedata = true
global file_frame = "datasets/"*ARGS[3]
detect = false
else
err("Не указано название датасета")
end
end
else
err("Не указан входной файл")
end
audio_input, freq = wavread(ARGS[1])
audio_input, freq = normalizeWAV(audio_input, freq)
# Исходя из частоты дискретизации получаем длину кадров и шаг между ними
window_length,step_length,window_function = windows(freq)
# Фильтруем кадры, отбрасывая кадры с низкой энергией
audio = filter(audio_input,window_length)
frames, freqs = size(audio)
if frames<5
err("Недостаточная энергия сигнала: "*ARGS[1])
end
# Осущетсвляем CQT-преобразование и сохранение спектограммы и массива данных
cqt_spectrogram = cqt(audio,freq)
# Преобразуем спектограмму в с спектр среднеквадратичных отклонений
raft_data = raft(cqt_spectrogram)
# Cохраняем результат
if savedata
#spectrogram(audio_input,freq)
save(raft_data,freq)
end
# Сравниваем с эталонами и выводим результат в stdout
if detect
println(determ(raft_data))
end

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1 Column1 Column2 Column3 Column4 Column5 Column6 Column7 Column8 Column9 Column10 Column11 Column12 Column13 Column14 Column15 Column16 Column17 Column18 Column19 Column20 Column21 Column22 Column23 Column24 Column25 Column26 Column27 Column28 Column29 Column30 Column31 Column32
2 0.00020231194394036388 0.00022174636359522038 0.00023622113110101275 0.0002523423642561494 0.00029113594984457884 0.0003465500885473935 0.00039826976762087255 0.0004279074198528977 0.0004233604026913774 0.00039266911400371034 0.00033336884156722504 0.0002659784428272092 0.00022508854766878692 0.00017763497659094857 0.00012824663228261094 9.080983488591606e-5 0.00011981877528438791 0.00021668247093902258 0.00033989867257576845 0.0004615137581893309 0.0005841333048239638 0.0006785446607103641 0.0006984768344582088 0.0006255647606217636 0.00046213670100477407 0.00023610824778555735 0.0002139391966445446 0.000487412389701432 0.0007657494183203286 0.0009573642564241277 0.0010562536995758938 0.0010616199582781608
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6 0.00015385233741817367 0.0001322378040863253 8.957699937674452e-5 7.946909811687784e-5 0.0001344346489148472 0.0002001165455778463 0.00024898059740765394 0.0002616593333706304 0.0002468806826817346 0.0001988720681314277 0.00013550711642754416 7.940489658504712e-5 5.1397728862182695e-5 6.142039784158276e-5 9.638737185885184e-5 0.00016535146809878746 0.0002576624545098284 0.00032892130292157224 0.00033471568193935874 0.00026189072097606783 0.00026439467686644445 0.0004697685517111327 0.0007408656879016057 0.0009531016509418237 0.0010609184920028227 0.0010393770524770461 0.0009445677531352938 0.0008492269660836126 0.0008009547704272413 0.0008033425660437238 0.0008055960351047562 0.000768444403741361
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12 0.0005744669561404278 0.0006313148606685113 0.0006659760007831039 0.0007034786968452532 0.0007556299831217484 0.0008126471008008208 0.0008632775898632332 0.0009092649977887285 0.00095847624872081 0.001010011273396407 0.0010524513702696 0.0010756918226894154 0.0010960042196788377 0.0011116651757068074 0.001109678631157236 0.0010720740983746607 0.0009974359638632514 0.0008864468230057524 0.0007138687330555097 0.0004650251365349876 0.00039997325667515105 0.0007671723688844907 0.0011561825871166916 0.0013773316489019902 0.0014143105972172767 0.0013637240212562986 0.001369357117610359 0.0015389724514609736 0.00186725849839957 0.0022162596226593598 0.002404778654647836 0.0023111420776952157
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15 0.0003059449281317778 0.0003487918965316051 0.00034025048500930415 0.00028715598914631665 0.00021452370629488924 0.00015871042909072116 0.00012919436404134913 8.276341067586602e-5 3.1996888422314194e-5 0.00019203759349471688 0.00039719184643416855 0.000603423445367431 0.0007641579922910393 0.000856856686357029 0.0009306183423031001 0.0010808544121916853 0.001342755572347414 0.0016964586319101465 0.002134751893461822 0.0026103203849223247 0.0029719541795322287 0.0030489755471189346 0.0027427050345707423 0.0021159259321390394 0.001405849415264901 0.0008975496212797242 0.0008010517583310984 0.0011511707365013284 0.0017409303325819127 0.0022836624373542575 0.002539654886705398 0.002406749368917956
16 0.00023548354001277092 0.00026492050117464376 0.00023800066451896176 0.00016227429390202225 7.610219248123558e-5 2.772391407718756e-5 2.445369643488171e-5 3.470902338995018e-5 0.0001070344051263985 0.00021591360423670018 0.00032317875755079626 0.00040406104673550516 0.00042364838616344074 0.000359794386675382 0.000200200899753512 0.00017872453894421023 0.000694840536611331 0.0014164398885746405 0.002224209107442964 0.002923932785095504 0.003294073842268439 0.003202428754708956 0.0026871370870813925 0.0019219308229495352 0.0011980304297969208 0.000726217110730581 0.000655944022847982 0.0010443715334713375 0.0015735949717040282 0.0019511269403520608 0.0020184917856016483 0.0017830320037632717
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140 0.00010777408888476588 3.435313174101265e-6 3.331769946040145e-5 0.0009187857642892108 6.893667605507582e-5 9.874082033287832e-6 9.585555938842957e-6 1.481745292718841e-6 3.861959519665904e-5 4.9732552210813545e-5 0.002359093206686633 0.0005094322699570567 5.940818931877705e-6 1.2066366714148937e-5 3.6296189723895834e-5 6.883439325370635e-6 8.168876626785633e-5 0.0011631284132422328 0.0004546437731667964 8.658123929996267e-5 5.9595750694354574e-5 8.428488847780146e-5 0.004773853327012053 1.2167785495157678e-5 0.00012162795696226408 0.0019191911991678066 1.294020435096247e-5 1.0194703392147451e-5 1.3250890205270298e-5 1.3219089578970191e-5 5.279704820585001e-6 3.514061313497182e-5 0.002447850790953107 6.175428620608381e-6 1.8130820998231134e-6 2.3382293019154585e-5 1.8951972335137612e-5 1.0737044164494394e-5 5.583079626500014e-6 0.0008551568278314554 0.0001467029272670264 8.06056483895686e-6 2.9382611326745414e-6 7.336950254707084e-6 8.66264150993239e-6 5.7667203837207566e-6 0.001446082482298321 0.0002679657268440645 3.96534869997842e-6 2.7859314812358174e-6 4.379908431624004e-5 2.1853831695747932e-5 4.508175147352237e-5 0.0005607149111997409 0.0023112420567947293 0.002215072886810264 4.488281117086304e-5 3.6007444387357757e-6 7.391207203453263e-6 6.675084576353118e-6 0.0003605962637094519 0.0006537390146397381 1.3019834724581556e-5 5.948711208160303e-6 5.822368275511138e-7 1.8936232901075731e-6 4.30125939945747e-6 3.506046686771514e-6 0.0016147380822089182 8.565002587612877e-6 3.1228930041033798e-6 4.322270161018287e-5 5.628728500996615e-5 9.587565829653666e-6 3.4556052261523626e-5 0.0019456300730416568 0.0002415104232434511 1.7508202611583993e-5 4.15040642989748e-6 2.3570948289446558e-6 5.312661852301465e-6 1.0369474018691217e-6 0.001068733015992495
141 2.2817198981259833e-5 1.0113323161935828e-5 1.1162274451547633e-5 0.0008934178385317943 6.549817254751687e-5 2.1288297511712142e-5 2.015462635115828e-5 1.0763728091466278e-5 3.5854286530545695e-5 8.776021752692388e-5 0.002325605107478721 0.00043925118903898476 2.074142041772805e-5 1.684142875187366e-5 5.2207209041970044e-5 1.568200064811457e-5 7.808774115950702e-5 0.0011056507581263242 0.0003643022224485089 9.577473089407357e-5 3.0849939286628154e-5 8.886396993544283e-5 0.007549358766071572 1.4763007445081468e-5 0.00012034252300086037 0.0018851048139822849 3.931421095998562e-5 7.093714614195461e-6 1.185077077882778e-5 1.599351437455434e-5 3.8110140336792658e-6 2.4130884637785356e-5 0.002446713243088958 5.739571084401788e-6 2.2670714600063672e-6 4.866742301428116e-5 2.4746017297088763e-5 2.817078838408399e-5 1.7345937578018167e-5 0.0008797873956181194 0.0001173781152718547 9.536930667400492e-6 1.0711304371392652e-5 5.6888127420815146e-6 1.8179980414896627e-6 8.378692987205361e-6 0.00140982171693482 0.00024814828838112455 3.219569465712507e-6 2.7062173959581244e-6 5.657815935252653e-5 1.3781082606202947e-5 2.1019275078320592e-5 0.0005023524743460382 0.002321438851012128 0.0021191763312067213 2.7949811019351394e-5 9.618871375815767e-7 3.3693528083948633e-6 5.411910541420137e-6 0.00030300268063213314 0.0006483491682385835 8.95850615469202e-6 2.5757883095297105e-6 7.450957372136245e-7 4.110002169764713e-6 2.023282486435046e-6 4.930401424023077e-6 0.0016132064612188962 3.6057540811942277e-6 2.9807477116020503e-6 1.3878507739721488e-5 4.101909256210915e-5 1.3408003763254293e-5 2.5225306799706994e-5 0.0019370481656115323 0.00022088210499458227 1.413945308049325e-5 7.106450992000795e-6 1.965480734332024e-6 6.1932700454987585e-6 7.716267414043444e-6 0.0010344435987978706
142 1.3348796512751453e-5 5.2295660348222455e-6 7.626130747756855e-6 0.0009005080473397377 2.934922648357794e-5 1.7074654148424382e-5 1.540268851806462e-5 1.0307937508170252e-5 4.1465100956610324e-5 9.196580152521095e-5 0.0022753444131342485 0.0003734881037195282 1.3276602203651811e-5 3.7614772478676494e-5 5.369495173944984e-5 3.900084855814947e-5 9.02850529341295e-5 0.0009508703065189381 0.00039610007992238493 7.065801438433857e-5 3.499363581849429e-5 0.00010187591032059336 0.005255622193930501 1.676555679190394e-5 9.228815909202864e-5 0.0018808512751680054 8.949991906729123e-6 1.2101950976918596e-5 8.831267530549342e-6 1.1895239194349773e-5 1.0550930474677497e-5 1.0309828114110445e-5 0.0024480390348365307 6.6363009016575196e-6 1.3889116373796636e-6 5.821159215054591e-5 1.3724222147666833e-5 3.5688826568731694e-5 1.7339886819257624e-5 0.0009012972653748108 8.432980944515658e-5 2.0825341501281367e-5 1.3160768902737673e-5 6.6329380621117345e-6 7.758184738962584e-6 1.1412013067774122e-5 0.0013781233065313775 0.00023034664231115344 6.383755931730372e-6 4.520314868924223e-6 2.1443720545914863e-5 1.7626860795345922e-5 2.374814301038183e-5 0.0004898021571141369 0.0023749398475799736 0.0018692933901246107 1.4819720115621992e-5 1.3210209875716703e-6 6.652399737583872e-6 7.392487846186746e-6 0.0002461547286507615 0.0006452878912067265 5.078821418677804e-6 1.0389877795328685e-5 7.782530157435558e-6 4.137379952579977e-6 7.281265650174676e-6 8.603727226614994e-6 0.0016121008625422767 1.5762128904276991e-6 5.3627297829336854e-6 1.7784498543400757e-5 2.1417809745751342e-5 2.9796640605723335e-5 1.953734141377313e-5 0.001920776630326478 0.00016642604777234536 1.1177511752505747e-5 1.2225763937163104e-5 5.796826742701309e-6 2.602771324001519e-6 6.75212095362379e-6 0.0010101382015580125
143 3.393525244210406e-5 2.0858574418725014e-5 4.72150439543816e-6 0.0009284318209477397 5.0977608221262346e-5 9.735674986528426e-6 3.1368375679765724e-6 4.172464638064804e-6 3.807679370545398e-5 4.5608660987697317e-5 0.0021727040765336897 0.0003468050284990238 9.192644734516048e-6 3.300164308906665e-5 1.3042593749002418e-5 1.5200352592058755e-5 1.5087245655944549e-5 0.0009431593529806303 0.0004490121692292985 1.7072766575115914e-5 2.1364266242061557e-5 8.935334039070542e-5 0.00626707042148087 6.521648529489038e-5 8.402523604973623e-5 0.001839728207999095 6.070494297120467e-6 1.1257596134033389e-5 9.121388745033337e-6 1.4011982095244542e-5 1.5369346203531525e-5 1.0190370254420514e-5 0.002450672093606534 3.520440549623306e-6 7.489278102893594e-6 2.3741401016921843e-5 6.936006020863374e-6 1.538727907311851e-5 3.9256721661996054e-6 0.0008867954988078475 7.626286076246125e-5 1.4518985488579455e-5 5.1520970053584815e-6 2.2937853561665726e-6 4.226466440108969e-6 1.7512903702878996e-6 0.0013340957525413706 0.00019567496683765257 5.04591122694252e-6 4.369049080579419e-6 6.656431488340892e-5 2.7763401668287777e-5 2.67000806987867e-5 0.0004571846544878535 0.0022563976592878655 0.001951571573843296 6.843821139718472e-6 3.398007100507071e-6 4.413860821415742e-6 8.309527848913667e-6 0.0001957547342106311 0.0006452684227610375 5.764619956166418e-7 4.110144514152377e-6 8.189985839276406e-6 5.1375348120446065e-6 3.3077697984650552e-6 6.173553367146943e-6 0.0016117193462436327 4.957695373961286e-6 3.667211199683208e-6 2.900796925468092e-5 1.3329861735631492e-5 9.214388135006084e-6 1.596940193823107e-5 0.001889412544503325 0.00015004612460451145 3.3447796640046105e-6 3.4483049970141254e-6 3.985509992825912e-6 5.144733206750194e-6 2.669641806155289e-6 0.0009640773952843939
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2 0.003989279830058364 0.003835876205976345 0.0038812334646262238 0.004052680805398061 0.004582836665484451 0.005313925627545566 0.006095042250453208 0.006739869452493654 0.007208153282552303 0.007573615991137718 0.007647453214168406 0.007341395322065955 0.007193010029495403 0.006721770430397017 0.006009249551389644 0.005548580641561852 0.005011339009530266 0.004297452357364499 0.0039353946482198416 0.003599311223442817 0.0028647994616390208 0.002056457451678309 0.0014592440038234341 0.0008201009406062024 0.0003524383157158574 0.00023764477133304726 0.0001282177351663886 0.00010686839825732349 0.00022665435447573312 0.00013365036903323208 9.266071234625293e-5 0.00015361696487061762 0.00023920720163129542 0.0001098510392423403 9.86394510757781e-5 0.00047287410680836106 0.0010470503172869516 0.0015752248434347687 0.002319670868891899 0.0031045769787114664 0.003485272951495842
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docs/analiticheskiy-obzor-i-klassifikatsiya-metodov-vydeleniya-priznakov-akusticheskogo-signala-v-rechevyh-sistemah.pdf Normal file
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#!/bin/bash
declare -i timeout=65 # default timeout in seconds.
Kill='/bin/kill'
split_line='-------------------------------------------------------------------------------'
success_msg='status OK'
ext_prg="arecord "$1
( ${ext_prg} )
cPID=$!
sleep 5
((timeout-=5))
status=0
if [[ ${timeout} -gt 0 ]]; then
while [[ $( ps | grep -o -G "^${cPID}"; ) -eq ${cPID} && ${status} -eq 0 ]]; do
status=$((timeout-- ? 0 : 1)); sleep 1
done
fi
if [[ ${status} -eq 1 ]]; then
eval '${Kill} -9 ${cPID}' &>/dev/null;
echo "${split_line}"
echo `basename $0`": Timeout occured, record saved."
echo "Process(${cPID}): ${ext_prg}"
echo "was killed because of timeout."
echo "${split_line}"
exit 1
else
echo "${split_line}"
echo -en "Process(${cPID}): ${ext_prg}\nterminate by itself successfully.\n"
rc=$(cat ${ftmp} | awk "/${success_msg}/"'{print $2}')
if [[ -z "${rc}" ]]; then
echo "${ext_prg}: ERR: no success code returned!!!"
exit 1
else
echo "${ext_prg}: return success code!!!"
fi
echo "${split_line}"
fi
exit 0

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include("./zaf.jl")
using .zaf
using WAV
using Statistics
using Plots
using Tables
using CSV
using DSP
using SignalAnalysis
using DataFrames
# Папка сохранения полученных датасетов
data_dir = "datasets/"
# Папка исходных аудио-файлов
files_dir = "files/"
# Настройки CQT преобразования
octave_resolution = 24
minimum_frequency = 55
maximum_frequency = 9520
time_resolution = 25
# Размеры кадра в сек
window_duration = 0.04;
# Уровень энергии для порогового фильтра
energy_level = 50
# Количество кадров для сглаживания скользящей средней слепка аудио-данных
m_offset = 20
# Чтение WAV-файла
function read()
if !isempty(ARGS)
try
return wavread(files_dir*ARGS[1])
catch e
println("Ошибка чтения wav-файла")
end
else
println("Не указан входной файл")
exit()
end
end
# Загрузка сохраненного файла данных очищенных окон с преобразованием в среднеквадратичное отклонение по частотным полосам
function load(filename)::Vector{Any}
if !isempty(filename)
try
data = CSV.read(filename, DataFrame, skipto=2)
return data[!, :Column1]
catch e
println("Ошибка чтения csv-файла")
end
else
println("Не указан входной файл")
exit()
end
end
# Скользящее среднее
function m_average(arr::Vector{Float64}, n::Number)::Vector{Any}
so_far = sum(arr[1:n])
out = zero(arr[n:end])
out[1] = so_far
for (i, (start, stop)) in enumerate(zip(arr, arr[n+1:end]))
so_far += stop - start
out[i+1] = so_far
end
return out
end
# Нормализация уровня
function normalize(audio_input::Matrix{Float64},freq::Number)
return mean(audio_input, dims=2), freq
end
# Разбитие на окна
function windows(freq::Number)
local window_length = nextpow(2, ceil(Int, window_duration*freq))
local window_function = zaf.hamming(window_length, "periodic") # оконная аналитическая функция
return window_length, convert(Int, window_length/2), window_function
end
# Фильтрация окон по уровню минимальной энергии звука в каждом окне
function filter(audio_input::Matrix{Float64},window_length::Number)::Matrix{Float64}
output_signal = zeros(1,1)
number_samples = length(audio_input)
padding_length = floor(Int, window_length / 2)
number_times =
ceil(
Int,
((number_samples + 2 * padding_length) - window_length) /
step_length,
) + 1
local i = 0
for j = 1:(number_times-10) # 10 последних кадров отбарсываем
frame = audio_input[i+1:i+window_length]
if energy(frame) > energy_level
output_signal = [output_signal;frame]
end
i = i + step_length
end
return output_signal
end
# Преобразование Фурье
function stft(audio::Matrix{Float64},freq::Number)
local window_length,step_length,window_function = windows(freq)
return zaf.stft(audio, window_function, step_length), window_length, step_length, window_function
end
# Спектограмма разложения ряда Фурье
function spectrogram(audio::Matrix{Float64},freq::Number)::Matrix{Float64}
local audio_stft, window_length = stft(audio,freq)
local audio_spectrogram = abs.(audio_stft[2:convert(Int, window_length/2)+1, :]) # Отбрасываем постоянную составляющую
local number_samples = length(audio_input)
local xtick_step = 1
local ytick_step = 1000
local plot_object = zaf.specshow(audio_spectrogram, number_samples, freq, xtick_step, ytick_step)
heatmap!(title = "Spectrogram (dB)", size = (990, 600))
savefig(plot_object, data_dir*ARGS[1]*"-"*"spectr.png")
#CSV.write(data_dir*ARGS[1]*"-"*"spectr.csv", Tables.table(audio_spectrogram))
return audio_spectrogram
end
# CQT-октавное вевлет-преобразование
function cqt(audio::Matrix{Float64},freq::Number)::Matrix{Float64}
local cqt_kernel = zaf.cqtkernel(freq, octave_resolution, minimum_frequency, maximum_frequency)
cqt_spectrogram = zaf.cqtspectrogram(audio, freq, time_resolution, cqt_kernel)
CSV.write(data_dir*ARGS[1]*"-"*"cqt.csv", Tables.table(cqt_spectrogram))
local xtick_step = 1
local plot_object = zaf.cqtspecshow(cqt_spectrogram, time_resolution, octave_resolution, minimum_frequency, xtick_step)
heatmap!(title = "CQT spectrogram (dB)", size = (990, 600))
savefig(plot_object, data_dir*ARGS[1]*"-"*"cqt.png")
return cqt_spectrogram
end
# Преобразование вевлет-спектрограммы в спектр среднего значения по октавам со сглаживанием
function raft(spectr::Matrix{Float64})::Vector{Any}
freqs, frames = size(spectr)
raft_data = zeros(freqs)
for i in 1:freqs
raft_data[i] = mean(spectr[i, :])
end
return m_average(raft_data,m_offset)
end
# Cохранение образа записи
function save(audio_data::Vector{Any},freq::Number)::Vector{Any}
CSV.write(data_dir*ARGS[1]*"-"*"analise.csv", Tables.table(audio_data))
numbers = length(audio_data)
xtick_locations = [0:octave_resolution:numbers;]
xtick_labels = convert(
Array{Int},
minimum_frequency * 2 .^ (xtick_locations / octave_resolution),
)
plot_object = plot(audio_data, xticks = (xtick_locations, xtick_labels))
plot!(title = "Sound form", size = (990, 600))
savefig(plot_object, data_dir*ARGS[1]*"-"*"analise.png")
return audio_data
end
# Считываем данные из WAV файла и нормализуем их, частота дискретизации получается из заголовка WAV-файла
audio_input, freq = read()
audio_input, freq = normalize(audio_input, freq)
# Исходя из частоты дискретизации получаем длину кадров и шаг между ними
window_length,step_length,window_function = windows(freq)
# Сохраняем спектограмму исходного сигнала
spectrogram(audio_input,freq)
# Фильтруем кадры, отбрасывая кадры с низкой энергией
audio = filter(audio_input,window_length)
frames, freqs = size(audio)
if frames<5
println("Файл не прошел фильтр суммарной энергии сигнала")
exit()
end
# Осущетсвляем CQT-преобразование и сохранение спектограммы и массива данных
cqt_spectrogram = cqt(audio,freq)
# Преобразуем спектограмму в с спектр среднеквадратичных отклонений
raft_data = raft(cqt_spectrogram)
# Cохраняем результат
save(raft_data,freq)