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"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Fish detection using Dynamic time warping and optimal transport\n",
"In this example, we'll search for a short audio clip in a a longer recording. The sound we're looking for is stored in the file `fish_pattern1.wav` and the recording we're looking through is stored in `fish_data.wav`. The fiels are available on [google drive](https://drive.google.com/open?id=1f2c6AjS9EY-boa6dT1v5aJrEpUoh_mIZ). We start by loading some packages"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(ENV[\"PYTHON\"], ENV[\"JULIA_NUM_THREADS\"], ENV[\"IJULIA_DEBUG\"]) = (\"python3\", \"4\", \"false\")\n"
]
},
{
"data": {
"text/plain": [
"(\"python3\", \"4\", \"false\")"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@show ENV[\"PYTHON\"], ENV[\"JULIA_NUM_THREADS\"], ENV[\"IJULIA_DEBUG\"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"using SpectralDistances, DynamicAxisWarping, DSP, TotalLeastSquares, WAV, LPVSpectral, Distances, Dates, Plots\n",
"using DynamicAxisWarping: lastlength\n",
"plotly()\n",
"function seconds2ms(s)\n",
" s = round(Int, s)\n",
" h = s÷3600\n",
" s -= 3600h\n",
" m = s÷60\n",
" s -= 60m\n",
" \"$m:$s\"\n",
"end\n",
"default(xformatter=seconds2ms, colorbar=false)\n",
"cd(@__DIR__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then set some parameters for the pre-processing. To cut down on processing time, we select a rather coarse resolution along both frequency and time axes. The radius `rad` is the maximum allowed time warping. Increasing this parameter makes the problem take longer time to solve."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"nfft = 256\n",
"rad = 6 # This is the maximum allowed warping radius\n",
"\n",
"timevec(res) = range(0,stop=length(y)/fs, length=length(res))\n",
"function n01(x) # Normalization for plotting\n",
" ql = quantile(filter(isfinite,x), 0.001)\n",
" x[x .< ql] .= ql\n",
" x = x .- ql\n",
" qu = quantile(filter(isfinite,x), 0.95)\n",
" x[x .> qu] .= qu\n",
" x ./= qu\n",
" x\n",
"end\n",
"ff(x,n=10) = filtfilt(ones(n),[n],x)\n",
"plotvecs(res) = timevec(res), n01(res);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're now ready to load the data and estimate some spectrograms. Here we take care to only estimate spectrograms for the frequencies that are improtant for the task, 200-550Hz."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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},
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"output_type": "display_data"
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