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https://github.com/alexbelgium/hassio-addons.git
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Clean spectral analysis
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alexbelgium
@@ -1,70 +0,0 @@
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import numpy as np
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import scipy.io.wavfile as wavfile
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import matplotlib.pyplot as plt
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import os
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import glob
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import sys # Import the sys module
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from utils.helpers import get_settings
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# Dependencies /usr/bin/pip install numpy scipy matplotlib
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# Define the directory containing the WAV files
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conf = get_settings()
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input_directory = os.path.join(conf['RECS_DIR'], 'StreamData')
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output_directory = os.path.join(conf['RECS_DIR'], 'Extracted/Charts')
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# Ensure the output directory exists
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if not os.path.exists(output_directory):
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os.makedirs(output_directory)
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# Check if a command-line argument is provided
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if len(sys.argv) > 1:
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# If an argument is provided, use it as the file to analyze
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wav_files = [sys.argv[1]]
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else:
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# If no argument is provided, analyze all WAV files in the directory
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wav_files = glob.glob(os.path.join(input_directory, '*.wav'))
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# Process each file
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for file_path in wav_files:
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# Load the WAV file
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sample_rate, audio_data = wavfile.read(file_path)
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# If stereo, select only one channel
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if len(audio_data.shape) > 1:
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audio_data = audio_data[:, 0]
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# Apply the Hamming window to the audio data
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hamming_window = np.hamming(len(audio_data))
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windowed_data = audio_data * hamming_window
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# Compute the FFT of the windowed audio data
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audio_fft = np.fft.fft(windowed_data)
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audio_fft = np.abs(audio_fft)
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# Compute the frequencies associated with the FFT values
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frequencies = np.fft.fftfreq(len(windowed_data), d=1/sample_rate)
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# Select the range of interest
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idx = np.where((frequencies >= 150) & (frequencies <= 15000))
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# Calculate the saturation threshold based on the bit depth
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bit_depth = audio_data.dtype.itemsize * 8
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max_amplitude = 2**(bit_depth - 1) - 1
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saturation_threshold = 0.8 * max_amplitude
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# Plot the spectrum with a logarithmic Y-axis
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plt.figure(figsize=(10, 6))
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plt.semilogy(frequencies[idx], audio_fft[idx], label='Spectrum')
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plt.axhline(y=saturation_threshold, color='r', linestyle='--', label='Saturation Threshold')
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plt.xlabel("Frequency (Hz)")
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plt.ylabel("Amplitude (Logarithmic)")
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plt.title(f"Frequency Spectrum (150 - 15000 Hz) - {os.path.basename(file_path)}")
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plt.legend()
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plt.grid(True)
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# Save the plot as a PNG file
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output_filename = os.path.basename(file_path).replace('.wav', '_spectrum.png')
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plt.savefig(os.path.join(output_directory, output_filename))
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plt.close() # Close the figure to free memory
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@@ -1,65 +0,0 @@
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#!/usr/bin/env bash
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# Performs the recording from the specified RTSP stream or soundcard
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set +u
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# shellcheck disable=SC1091
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source /etc/birdnet/birdnet.conf
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# Read the logging level from the configuration option
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# shellcheck disable=SC2154
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LOGGING_LEVEL="${LogLevel_BirdnetRecordingService:-error}"
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# If empty for some reason default to log level of error
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[ -z "$LOGGING_LEVEL" ] && LOGGING_LEVEL='error'
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# Additionally if we're at debug or info level then allow printing of script commands and variables
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if [ "$LOGGING_LEVEL" == "info" ] || [ "$LOGGING_LEVEL" == "debug" ];then
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# Enable printing of commands/variables etc to terminal for debugging
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set -x
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fi
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[ -z "$RECORDING_LENGTH" ] && RECORDING_LENGTH=15
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[ -d "$RECS_DIR"/StreamData ] || mkdir -p "$RECS_DIR"/StreamData
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filename="Spectrum_$(date "+%Y-%m-%d_%H:%M").wav"
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if [ -n "${RTSP_STREAM:-}" ];then
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# Explode the RSPT steam setting into an array so we can count the number we have
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RTSP_STREAMS_EXPLODED_ARRAY=("${RTSP_STREAM//,/ }")
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while true;do
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# Initially start the count off at 1 - our very first stream
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RTSP_STREAMS_STARTED_COUNT=1
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FFMPEG_PARAMS=""
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# Loop over the streams
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for i in "${RTSP_STREAMS_EXPLODED_ARRAY[@]}"
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do
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# Map id used to map input to output (first stream being 0), this is 0 based in ffmpeg so decrement our counter (which is more human readable) by 1
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MAP_ID="$((RTSP_STREAMS_STARTED_COUNT-1))"
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# Build up the parameters to process the RSTP stream, including mapping for the output
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FFMPEG_PARAMS+="-vn -thread_queue_size 512 -i ${i} -map ${MAP_ID}:a:0 -t ${RECORDING_LENGTH} -acodec pcm_s16le -ac 2 -ar 48000 file:${RECS_DIR}/StreamData/$filename "
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# Increment counter
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((RTSP_STREAMS_STARTED_COUNT += 1))
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done
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# Make sure were passing something valid to ffmpeg, ffmpeg will run interactive and control our loop by waiting ${RECORDING_LENGTH} between loops because it will stop once that much has been recorded
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if [ -n "$FFMPEG_PARAMS" ];then
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ffmpeg -hide_banner -loglevel "$LOGGING_LEVEL" -nostdin "$FFMPEG_PARAMS"
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fi
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done
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else
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if pgrep arecord &> /dev/null ;then
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echo "Recording"
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else
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if [ -z "${REC_CARD}" ];then
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arecord -f S16_LE -c"${CHANNELS}" -r48000 -t wav --max-file-time "${RECORDING_LENGTH}"\
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--use-strftime "${RECS_DIR}"/StreamData/"$filename"
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else
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arecord -f S16_LE -c"${CHANNELS}" -r48000 -t wav --max-file-time "${RECORDING_LENGTH}"\
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-D "${REC_CARD}" --use-strftime "${RECS_DIR}"/StreamData/"$filename"
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fi
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fi
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fi
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# Create the spectral analysis
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"$PYTHON_VIRTUAL_ENV" "$HOME"/BirdNET-Pi/scripts/spectral_analysis.py
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