hassio-addons/birdnet-pi/DOCS.md

Microphone considerations

The critical element is the microphone quality : a Boya By-lm 40 or clippy EM272 (with a very good aux-usb converter) is key to improve the quality of detections. Here is some example tests I did (whole threads are really interesting also): https://github.com/mcguirepr89/BirdNET-Pi/discussions/39#discussioncomment-9706951 https://github.com/mcguirepr89/BirdNET-Pi/discussions/1092#discussioncomment-9706191

My recommendation :

• Best entry system (< 50€) : Boya By-lm40 (30€) + deadcat (10 €)
• Best middle end system (< 150 €) : Clippy EM272 TRS/TRRS (55€) + Rode AI micro trs/trrs to usb (70€) + Rycote deadcat (27€)
• Best high end system (<400 €) : Clippy EM272 XLR (85€) or LOM Ucho Pro (75€) + Focusrite Scarlet 2i2 4th Gen (200€) + Bubblebee Pro Extreme deadcat (45€)

App settings recommendation

I've tested lots of settings by running 2 versions of my HA birdnet-pi addon in parallel using the same rtsp feed, and comparing impact of parameters. My conclusions aren't universal, as it seems to be highly dependent on the region and type of mic used. For example, the old model seems to be better in Australia, while the new one better in Europe.

• Model
  • Version : 6k_v2,4 (performs better in Europe at least, the 6k performs better in Australia)
  • Species range model : v1 (uncheck v2.4 ; seems more robust in Europe)
  • Species occurence threshold : 0,001 (was 0,00015 using v2.4 ; use the Species List Tester to check the correct value for you)
• Audio settings
  • Default
  • Channel : 1 (doesn't really matter as analysis is made on mono signal ; 1 allows decreased saved audio size but seems to give slightly messed up spectrograms in my experience)
  • Recording Length : 18 (that's because I use an overlap of 0,5 ; so it analysis 0-3s ; 2,5-5,5s ; 5-8s ; 7,5-10,5 ; 10-13 ; 12,5-15,5 ; 15-18)
  • Extraction Length : 9s (could be 6, but I like to hear my birds :-))
  • Audio format : mp3 (why bother with something else)
• Birdnet-lite settings
  • Overlap : 0,5s
  • Minimum confidence : 0,7
  • Sigmoid sensitivity : 1,25 (I've tried 1,00 but it gave much more false positives ; as decreasing this value increases sensitivity)

Set RTSP server

Inspired by : https://github.com/mcguirepr89/BirdNET-Pi/discussions/1006#discussioncomment-6747450

On your desktop

• Download imager
• Install raspbian lite 64

With ssh, install requisite softwares

# Update

sudo apt-get update -y
sudo apt-get dist-upgrade -y

# Disable useless services
sudo systemctl disable hciuart
sudo systemctl disable bluetooth
sudo systemctl disable triggerhappy
sudo systemctl disable avahi-daemon
sudo systemctl disable dphys-swapfile

# Install RTSP server
sudo apt-get install -y micro ffmpeg lsof
sudo -s cd /root && wget -c https://github.com/bluenviron/mediamtx/releases/download/v1.9.1/mediamtx_v1.9.1_linux_arm64v8.tar.gz -O - | sudo tar -xz

Configure Audio

Find right device

# List audio devices
arecord -l

# Check audio device parameters. Example :
arecord -D hw:1,0 --dump-hw-params

Add startup script

sudo nano startmic.sh && chmod +x startmic.sh

#!/bin/bash
echo "Starting birdmic"

# Disable gigabit ethernet
sudo ethtool -s eth0 speed 100 duplex full autoneg on

# Run GStreamer RTSP server if installed
if command -v gst-launch-1.0 &>/dev/null; then
    ./rtsp_audio_server.py & sleep 2 >/tmp/log_rtsp 2>/tmp/log_rtsp_error &
    gst_pid=$!
else
    echo "GStreamer not found, skipping to ffmpeg fallback"
    gst_pid=0
fi

# Wait for a moment to let GStreamer initialize
sleep 5

# Check if the RTSP stream can be accessed (i.e., the feed can be read)
if ! ffmpeg -rtsp_transport tcp -i rtsp://localhost:8554/birdmic -t 1 -f null - > /dev/null 2>&1; then
    echo "GStreamer RTSP stream is not accessible, switching to ffmpeg"
    
    # Kill the GStreamer process if it's still running
    if [ "$gst_pid" -ne 0 ]; then
        kill "$gst_pid"
    fi
    
    # Start mediamtx first and give it a moment to initialize
    ./mediamtx & 
    sleep 5
    
    # Run ffmpeg as fallback
    ffmpeg -nostdin -use_wallclock_as_timestamps 1 -fflags +genpts -f alsa -acodec pcm_s16be -ac 2 -ar 96000 \
        -i plughw:0,0 -ac 2 -f rtsp -acodec pcm_s16be rtsp://localhost:8554/birdmic -rtsp_transport tcp \
        -buffer_size 512k 2>/tmp/rtsp_error &
else
    echo "GStreamer RTSP stream is running successfully"
fi

# Set microphone volume
sleep 5
MICROPHONE_NAME="Line In 1 Gain" # for Focusrite Scarlett 2i2
sudo amixer -c 0 sset "$MICROPHONE_NAME" 40

sleep 60

# Run focusrite and autogain scripts if present
if [ -f "$HOME/focusrite.sh" ]; then
    "$HOME/focusrite.sh" >/tmp/log_focusrite 2>/tmp/log_focusrite_error &
fi

if [ -f "$HOME/autogain.py" ]; then
    "$HOME/autogain.py" >/tmp/log_autogain 2>/tmp/log_autogain_error &
fi

Optional : use gstreamer instead of ffmpeg

# Install gstreamer
sudo apt-get update
#sudo apt-get install -y \
#  gstreamer1.0-rtsp \
#  gstreamer1.0-tools \
#  gstreamer1.0-alsa \
#  gstreamer1.0-plugins-base \
#  gstreamer1.0-plugins-good \
#  gstreamer1.0-plugins-bad \
#  gstreamer1.0-plugins-ugly \
#  gstreamer1.0-libav
apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio -y

Create a script named rtsp_audio_server.py

#!/usr/bin/env python3

import gi
import sys
import logging
import os
import signal

gi.require_version('Gst', '1.0')
gi.require_version('GstRtspServer', '1.0')

from gi.repository import Gst, GstRtspServer, GLib

# Initialize GStreamer
Gst.init(None)

# Configure Logging
LOG_FILE = "gst_rtsp_server.log"
logging.basicConfig(
    filename=LOG_FILE,
    filemode='a',
    format='%(asctime)s %(levelname)s: %(message)s',
    level=logging.DEBUG  # Set to DEBUG for comprehensive logging
)
logger = logging.getLogger(__name__)

class AudioFactory(GstRtspServer.RTSPMediaFactory):
    def __init__(self):
        super(AudioFactory, self).__init__()
        self.set_shared(True)          # Allow multiple clients to access the stream
        self.set_latency(500)          # Increase latency to 500ms to improve stream stability
        self.set_suspend_mode(GstRtspServer.RTSPSuspendMode.NONE)  # Prevent suspension of the stream when no clients are connected
        logger.debug("AudioFactory initialized: shared=True, latency=500ms, suspend_mode=NONE.")

    def do_create_element(self, url):
        """
        Create and return the GStreamer pipeline for streaming audio.
        """
        pipeline_str = (
            "alsasrc device=plughw:0,0 do-timestamp=true buffer-time=2000000 latency-time=1000000 ! "  # Increased buffer size
            "queue max-size-buffers=0 max-size-bytes=0 max-size-time=0 ! "         # Add queue to handle buffer management
            "audioconvert ! "                                # Convert audio to a suitable format
            "audioresample ! "                               # Resample audio if necessary
            "audio/x-raw,format=S16BE,channels=2,rate=48000 ! "  # Set audio properties (rate = 48kHz)
            "rtpL16pay name=pay0 pt=96"                     # Payload for RTP
        )
        logger.debug(f"Creating GStreamer pipeline: {pipeline_str}")
        try:
            pipeline = Gst.parse_launch(pipeline_str)
            if not pipeline:
                logger.error("Failed to parse GStreamer pipeline.")
                return None
            return pipeline
        except Exception as e:
            logger.error(f"Exception while creating pipeline: {e}")
            return None

class GstServer:
    def __init__(self):
        self.server = GstRtspServer.RTSPServer()
        self.server.set_service("8554")      # Set the RTSP server port
        self.server.set_address("0.0.0.0")   # Listen on all network interfaces
        logger.debug("RTSP server configured: address=0.0.0.0, port=8554.")

        factory = AudioFactory()
        mount_points = self.server.get_mount_points()
        mount_points.add_factory("/birdmic", factory)  # Mount point
        logger.debug("Factory mounted at /birdmic.")

        self.server.attach(None)  # Attach the server to the default main context
        logger.info("RTSP server attached and running.")

def main():
    # Create GstServer instance
    server = GstServer()
    print("RTSP server is running at rtsp://localhost:8554/birdmic")
    logger.info("RTSP server is running at rtsp://localhost:8554/birdmic")

    # Set up the main loop with proper logging
    loop = GLib.MainLoop()

    # Handle termination signals to ensure graceful shutdown
    def shutdown(signum, frame):
        logger.info(f"Shutting down RTSP server due to signal {signum}.")
        print("\nShutting down RTSP server.")
        loop.quit()

    # Register signal handlers for graceful termination
    signal.signal(signal.SIGINT, shutdown)
    signal.signal(signal.SIGTERM, shutdown)

    try:
        loop.run()
    except Exception as e:
        logger.error(f"Main loop encountered an exception: {e}")
    finally:
        logger.info("RTSP server has been shut down.")

if __name__ == "__main__":
    # Ensure log file exists
    if not os.path.exists(LOG_FILE):
        open(LOG_FILE, 'w').close()

    main()

Optional : Startup automatically

chmod +x startmic.sh
crontab -e # select nano as your editor

Paste in @reboot $HOME/startmic.sh then save and exit nano. Reboot the Pi and test again with VLC to make sure the RTSP stream is live.

Optional : optimize config.txt

sudo nano /boot/firmware/config.txt

# Enable audio and USB optimizations
dtparam=audio=off          # Disable the default onboard audio to prevent conflicts
dtoverlay=disable-bt        # Disable onboard Bluetooth to reduce USB bandwidth usage
dtoverlay=disable-wifi      # Disable onboard wifi
# Limit Ethernet to 100 Mbps (disable Gigabit Ethernet)
dtparam=eth_max_speed=100
# USB optimizations
dwc_otg.fiq_fix_enable=1    # Enable FIQ (Fast Interrupt) handling for improved USB performance
max_usb_current=1           # Increase the available USB current (required if Scarlett is powered over USB)
# Additional audio settings (for low-latency operation)
avoid_pwm_pll=1             # Use a more stable PLL for the audio clock
# Optional: HDMI and other settings can be turned off if not needed
hdmi_blanking=1             # Disable HDMI (save power and reduce interference)

Optional : install Focusrite driver

sudo apt-get install make linux-headers-$(uname -r)
curl -LO https://github.com/geoffreybennett/scarlett-gen2/releases/download/v6.9-v1.3/snd-usb-audio-kmod-6.6-v1.3.tar.gz
tar -xzf snd-usb-audio-kmod-6.6-v1.3.tar.gz
cd snd-usb-audio-kmod-6.6-v1.3
KSRCDIR=/lib/modules/$(uname -r)/build
make -j4 -C $KSRCDIR M=$(pwd) clean
make -j4 -C $KSRCDIR M=$(pwd)
sudo make -j4 -C $KSRCDIR M=$(pwd) INSTALL_MOD_DIR=updates/snd-usb-audio modules_install
sudo depmod
sudo reboot
dmesg | grep -A 5 -B 5 -i focusrite

Optional : add RAM disk

sudo cp /usr/share/systemd/tmp.mount /etc/systemd/system/tmp.mount
sudo systemctl enable tmp.mount
sudo systemctl start tmp.mount

Optional : Configuration for Focusrite Scarlett 2i2

Add this content in "$HOME/focusrite.sh" && chmod +x "$HOME/focusrite.sh"

#!/bin/bash

# Set PCM controls for capture
sudo amixer -c 0 cset numid=31 'Analogue 1'  # 'PCM 01' - Set to 'Analogue 1'
sudo amixer -c 0 cset numid=32 'Analogue 1'  # 'PCM 02' - Set to 'Analogue 1'
sudo amixer -c 0 cset numid=33 'Off'         # 'PCM 03' - Disabled
sudo amixer -c 0 cset numid=34 'Off'         # 'PCM 04' - Disabled

# Set DSP Input controls (Unused, set to Off)
sudo amixer -c 0 cset numid=29 'Off'         # 'DSP Input 1'
sudo amixer -c 0 cset numid=30 'Off'         # 'DSP Input 2'

# Configure Line In 1 as main input for mono setup
sudo amixer -c 0 cset numid=8 'Off'          # 'Line In 1 Air' - Keep 'Off'
sudo amixer -c 0 cset numid=14 off           # 'Line In 1 Autogain' - Disabled
sudo amixer -c 0 cset numid=6 'Line'         # 'Line In 1 Level' - Set level to 'Line'
sudo amixer -c 0 cset numid=21 on           # 'Line In 1 Safe' - Enabled to avoid clipping / noise impact ?

# Disable Line In 2 to minimize interference (if not used)
sudo amixer -c 0 cset numid=9 'Off'          # 'Line In 2 Air'
sudo amixer -c 0 cset numid=17 off           # 'Line In 2 Autogain' - Disabled
sudo amixer -c 0 cset numid=16 0             # 'Line In 2 Gain' - Set gain to 0 (mute)
sudo amixer -c 0 cset numid=7 'Line'         # 'Line In 2 Level' - Set to 'Line'
sudo amixer -c 0 cset numid=22 off           # 'Line In 2 Safe' - Disabled

# Set Line In 1-2 controls
sudo amixer -c 0 cset numid=12 off           # 'Line In 1-2 Link' - No need to link for mono
sudo amixer -c 0 cset numid=10 on            # 'Line In 1-2 Phantom Power' - Enabled for condenser mics

# Set Analogue Outputs to use the same mix for both channels (Mono setup)
sudo amixer -c 0 cset numid=23 'Mix A'       # 'Analogue Output 01' - Set to 'Mix A'
sudo amixer -c 0 cset numid=24 'Mix A'       # 'Analogue Output 02' - Same mix as Output 01

# Set Direct Monitor to off to prevent feedback
sudo amixer -c 0 cset numid=53 'Off'         # 'Direct Monitor'

# Set Input Select to Input 1
sudo amixer -c 0 cset numid=11 'Input 1'     # 'Input Select'

# Optimize Monitor Mix settings for mono output
sudo amixer -c 0 cset numid=54 153           # 'Monitor 1 Mix A Input 01' - Set to 153 (around -3.50 dB)
sudo amixer -c 0 cset numid=55 153           # 'Monitor 1 Mix A Input 02' - Set to 153 for balanced output
sudo amixer -c 0 cset numid=56 0             # 'Monitor 1 Mix A Input 03' - Mute unused channels
sudo amixer -c 0 cset numid=57 0             # 'Monitor 1 Mix A Input 04'

# Set Sync Status to Locked
sudo amixer -c 0 cset numid=52 'Locked'      # 'Sync Status'

echo "Mono optimization applied. Only using primary input and balanced outputs."

Optional : Autogain script for microphone

Add this content in "$HOME/autogain.py" && chmod +x "$HOME/autogain.py"

#!/usr/bin/env python3
"""
Microphone Gain Adjustment Script with THD and Overload Detection

This script captures audio from an RTSP stream, processes it to calculate the RMS
within the 2000-8000 Hz frequency band, detects clipping, calculates Total Harmonic
Distortion (THD), and adjusts the microphone gain based on predefined noise thresholds,
trends, and distortion metrics.

Dependencies:
- numpy
- scipy
- ffmpeg (installed and accessible in PATH)
- amixer (for microphone gain control)

Author: OpenAI ChatGPT
Date: 2024-04-27 (Updated)
"""

import subprocess
import numpy as np
from scipy.signal import butter, sosfilt, find_peaks
import time
import re

# ---------------------------- Configuration ----------------------------

# Microphone Settings
MICROPHONE_NAME = "Line In 1 Gain"  # Adjust to match your microphone's control name
MIN_GAIN_DB = 20                    # Minimum gain in dB
MAX_GAIN_DB = 45                    # Maximum gain in dB
DECREASE_GAIN_STEP_DB = 1           # Gain decrease step in dB
INCREASE_GAIN_STEP_DB = 5           # Gain increase step in dB
CLIPPING_REDUCTION_DB = 3           # Reduction in dB if clipping is detected

# Noise Thresholds
NOISE_THRESHOLD_HIGH = 0.001        # Upper threshold for noise RMS amplitude
NOISE_THRESHOLD_LOW = 0.00035       # Lower threshold for noise RMS amplitude

# Trend Detection
TREND_COUNT_THRESHOLD = 3           # Number of consecutive trends needed to adjust gain

# RTSP Stream URL
RTSP_URL = "rtsp://192.168.178.124:8554/birdmic"  # Replace with your RTSP stream URL

# Debug Mode (1 for enabled, 0 for disabled)
DEBUG = 1

# Microphone Characteristics
MIC_SENSITIVITY_DB = -28  # dB (0 dB = 1V/Pa)
MIC_CLIPPING_SPL = 120    # dB SPL at 1 kHz

# Calibration Constants (These may need to be adjusted based on actual calibration)
REFERENCE_PRESSURE = 20e-6  # 20 µPa, standard reference for SPL

# THD Settings
THD_FUNDAMENTAL_THRESHOLD_DB = 60  # Minimum SPL to consider THD calculation
MAX_THD_PERCENTAGE = 5.0           # Maximum acceptable THD percentage

# -----------------------------------------------------------------------


def debug_print(msg):
    """
    Prints debug messages if DEBUG mode is enabled.

    :param msg: The debug message to print.
    """
    if DEBUG:
        current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
        print(f"[{current_time}] [DEBUG] {msg}")


def get_gain_db(mic_name):
    """
    Retrieves the current gain setting of the specified microphone using amixer.

    :param mic_name: The name of the microphone control in amixer.
    :return: The current gain in dB as a float, or None if retrieval fails.
    """
    cmd = ['amixer', 'sget', mic_name]
    try:
        output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode()
        # Regex to find patterns like [30.00dB]
        match = re.search(r'\[(-?\d+(\.\d+)?)dB\]', output)
        if match:
            gain_db = float(match.group(1))
            debug_print(f"Retrieved gain: {gain_db} dB")
            return gain_db
        else:
            debug_print("No gain information found in amixer output.")
            return None
    except subprocess.CalledProcessError as e:
        debug_print(f"amixer sget failed: {e}")
        return None


def set_gain_db(mic_name, gain_db):
    """
    Sets the gain of the specified microphone using amixer.

    :param mic_name: The name of the microphone control in amixer.
    :param gain_db: The desired gain in dB.
    :return: True if the gain was set successfully, False otherwise.
    """
    cmd = ['amixer', 'sset', mic_name, f'{gain_db}dB']
    try:
        subprocess.check_call(cmd, stderr=subprocess.STDOUT)
        debug_print(f"Set gain to: {gain_db} dB")
        return True
    except subprocess.CalledProcessError as e:
        debug_print(f"amixer sset failed: {e}")
        return False


def find_fundamental_frequency(fft_freqs, fft_magnitude, min_freq=1000, max_freq=8000):
    """
    Dynamically finds the fundamental frequency within a specified range.

    :param fft_freqs: Array of frequency bins from FFT.
    :param fft_magnitude: Magnitude spectrum from FFT.
    :param min_freq: Minimum frequency to search for the fundamental.
    :param max_freq: Maximum frequency to search for the fundamental.
    :return: Fundamental frequency in Hz and its amplitude.
    """
    # Limit search to the specified frequency range
    idx_min = np.searchsorted(fft_freqs, min_freq)
    idx_max = np.searchsorted(fft_freqs, max_freq)
    if idx_max <= idx_min:
        return None, 0

    search_magnitude = fft_magnitude[idx_min:idx_max]
    search_freqs = fft_freqs[idx_min:idx_max]

    # Find peaks in the magnitude spectrum
    peaks, properties = find_peaks(search_magnitude, height=np.max(search_magnitude) * 0.1)
    if len(peaks) == 0:
        return None, 0

    # Identify the peak with the highest magnitude
    peak_heights = properties['peak_heights']
    max_peak_idx = np.argmax(peak_heights)
    fundamental_freq = search_freqs[peaks[max_peak_idx]]
    fundamental_amplitude = search_magnitude[peaks[max_peak_idx]]

    debug_print(f"Detected fundamental frequency: {fundamental_freq:.2f} Hz with amplitude {fundamental_amplitude:.4f}")
    return fundamental_freq, fundamental_amplitude


def thd_calculation(audio, sampling_rate, num_harmonics=5):
    """
    Calculates Total Harmonic Distortion (THD) for the audio signal.

    :param audio: The audio signal as a numpy array.
    :param sampling_rate: Sampling rate of the audio signal.
    :param num_harmonics: Number of harmonics to include in THD calculation.
    :return: THD value in percentage.
    """
    # FFT analysis
    fft_vals = np.fft.rfft(audio)
    fft_freqs = np.fft.rfftfreq(len(audio), 1 / sampling_rate)
    fft_magnitude = np.abs(fft_vals)

    # Dynamically find the fundamental frequency
    fundamental_freq, fundamental_amplitude = find_fundamental_frequency(fft_freqs, fft_magnitude)

    if fundamental_freq is None or fundamental_amplitude < 1e-6:
        debug_print("Fundamental frequency not detected or amplitude too low. Skipping THD calculation.")
        return 0.0

    # Calculate harmonic amplitudes
    harmonic_amplitudes = []
    for n in range(2, num_harmonics + 1):
        harmonic_freq = n * fundamental_freq
        if harmonic_freq > sampling_rate / 2:
            break  # Skip harmonics beyond Nyquist frequency

        # Find the closest frequency bin
        harmonic_idx = np.argmin(np.abs(fft_freqs - harmonic_freq))
        harmonic_amp = fft_magnitude[harmonic_idx]
        harmonic_amplitudes.append(harmonic_amp)
        debug_print(f"Harmonic {n} frequency: {harmonic_freq:.2f} Hz, amplitude: {harmonic_amp:.4f}")

    # Calculate THD
    harmonic_sum = np.sqrt(np.sum(np.square(harmonic_amplitudes)))
    if fundamental_amplitude == 0:
        thd = 0.0
    else:
        thd = (harmonic_sum / fundamental_amplitude) * 100  # THD in percentage

    debug_print(f"THD Calculation: {thd:.2f}%")
    return thd


def calculate_spl(audio, mic_sensitivity_db):
    """
    Calculates the Sound Pressure Level (SPL) from the audio signal.

    :param audio: The audio signal as a numpy array.
    :param mic_sensitivity_db: Microphone sensitivity in dB (0 dB = 1V/Pa).
    :return: SPL in dB.
    """
    # Calculate RMS amplitude
    rms_amplitude = np.sqrt(np.mean(audio ** 2))
    if rms_amplitude == 0:
        debug_print("RMS amplitude is zero. SPL cannot be calculated.")
        return -np.inf

    # Convert RMS amplitude to voltage
    # Assuming audio is normalized between -1 and 1, representing the actual voltage would require calibration
    # For demonstration, we'll proceed with the given sensitivity

    # Convert voltage to pressure (Pa)
    mic_sensitivity_linear = 10 ** (mic_sensitivity_db / 20)  # V/Pa
    pressure = rms_amplitude / mic_sensitivity_linear  # Pa

    # Calculate SPL
    spl = 20 * np.log10(pressure / REFERENCE_PRESSURE)
    debug_print(f"Calculated SPL: {spl:.2f} dB")
    return spl


def detect_microphone_overload(spl, mic_clipping_spl):
    """
    Detects if the calculated SPL is approaching the microphone's clipping SPL.

    :param spl: The calculated SPL.
    :param mic_clipping_spl: The microphone's clipping SPL.
    :return: True if overload is detected, False otherwise.
    """
    if spl >= mic_clipping_spl - 3:  # Consider overload if within 3 dB of clipping SPL
        debug_print("Microphone overload detected.")
        return True
    return False


def calculate_noise_rms_and_thd(rtsp_url, bandpass_sos, sampling_rate, num_bins=5):
    """
    Captures audio from an RTSP stream, calculates RMS, THD, and SPL, and detects microphone overload.

    :param rtsp_url: The RTSP stream URL.
    :param bandpass_sos: Precomputed bandpass filter coefficients (Second-Order Sections).
    :param sampling_rate: Sampling rate of the audio signal.
    :param num_bins: Number of segments to divide the audio into.
    :return: Tuple containing the RMS amplitude, THD percentage, SPL value, and overload status.
    """
    cmd = [
        'ffmpeg',
        '-loglevel', 'error',
        '-rtsp_transport', 'tcp',
        '-i', rtsp_url,
        '-vn',
        '-f', 's16le',
        '-acodec', 'pcm_s16le',
        '-ar', str(sampling_rate),
        '-ac', '1',
        '-t', '5',
        '-'
    ]

    try:
        debug_print(f"Starting audio capture from {rtsp_url}")
        process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        stdout, stderr = process.communicate()

        if process.returncode != 0:
            debug_print(f"ffmpeg failed with error: {stderr.decode()}")
            return None, None, None, False

        # Convert raw PCM data to numpy array
        audio = np.frombuffer(stdout, dtype=np.int16).astype(np.float32) / 32768.0
        debug_print(f"Captured {len(audio)} samples from audio stream.")

        if len(audio) == 0:
            debug_print("No audio data captured.")
            return None, None, None, False

        # Apply bandpass filter
        filtered_audio = sosfilt(bandpass_sos, audio)
        debug_print("Applied bandpass filter to audio data.")

        # Calculate RMS
        rms_amplitude = np.sqrt(np.mean(filtered_audio ** 2))

        # Calculate THD
        thd_percentage = thd_calculation(filtered_audio, sampling_rate)

        # Calculate SPL
        spl = calculate_spl(filtered_audio, MIC_SENSITIVITY_DB)

        # Detect microphone overload
        overload = detect_microphone_overload(spl, MIC_CLIPPING_SPL)

        return rms_amplitude, thd_percentage, spl, overload

    except Exception as e:
        debug_print(f"Exception during audio processing: {e}")
        return None, None, None, False


def main():
    """
    Main loop that continuously monitors background noise, detects clipping, calculates THD,
    and adjusts microphone gain accordingly.
    """
    TREND_COUNT = 0
    PREVIOUS_TREND = 0

    # Precompute the bandpass filter coefficients
    LOWCUT = 2000    # Lower frequency bound in Hz
    HIGHCUT = 8000   # Upper frequency bound in Hz
    FILTER_ORDER = 5  # Order of the Butterworth filter
    SAMPLING_RATE = 32000  # Sampling rate in Hz

    sos = butter(FILTER_ORDER, [LOWCUT, HIGHCUT], btype='band', fs=SAMPLING_RATE, output='sos')
    debug_print("Precomputed Butterworth bandpass filter coefficients.")

    # Set the microphone gain to the maximum gain at the start
    success = set_gain_db(MICROPHONE_NAME, MAX_GAIN_DB)
    if success:
        print(f"Microphone gain set to {MAX_GAIN_DB} dB at start.")
    else:
        print("Failed to set microphone gain at start. Exiting.")
        return

    while True:
        rms, thd, spl, overload = calculate_noise_rms_and_thd(RTSP_URL, sos, SAMPLING_RATE)

        if rms is None:
            print("Failed to compute noise RMS. Retrying in 1 minute...")
            time.sleep(60)
            continue

        # Print the final converted RMS amplitude
        print(f"Converted RMS Amplitude: {rms:.6f}")
        debug_print(f"Current background noise (RMS amplitude): {rms:.6f}")

        # Detect clipping and reduce gain if needed
        if overload:
            current_gain_db = get_gain_db(MICROPHONE_NAME)
            if current_gain_db is not None:
                NEW_GAIN_DB = current_gain_db - CLIPPING_REDUCTION_DB
                if NEW_GAIN_DB < MIN_GAIN_DB:
                    NEW_GAIN_DB = MIN_GAIN_DB
                success = set_gain_db(MICROPHONE_NAME, NEW_GAIN_DB)
                if success:
                    print(f"Clipping detected. Reduced gain to {NEW_GAIN_DB} dB")
                    debug_print(f"Gain reduced to {NEW_GAIN_DB} dB due to clipping.")
                else:
                    print("Failed to reduce gain due to clipping.")
            # Skip trend adjustment in case of clipping
            time.sleep(60)
            continue

        # Handle THD if SPL is above a reasonable threshold
        if spl >= THD_FUNDAMENTAL_THRESHOLD_DB:
            if thd > MAX_THD_PERCENTAGE:
                debug_print(f"High THD detected: {thd:.2f}%")
                current_gain_db = get_gain_db(MICROPHONE_NAME)
                if current_gain_db is not None:
                    NEW_GAIN_DB = current_gain_db - DECREASE_GAIN_STEP_DB
                    if NEW_GAIN_DB < MIN_GAIN_DB:
                        NEW_GAIN_DB = MIN_GAIN_DB
                    success = set_gain_db(MICROPHONE_NAME, NEW_GAIN_DB)
                    if success:
                        print(f"High THD detected. Decreased gain to {NEW_GAIN_DB} dB")
                        debug_print(f"Gain decreased to {NEW_GAIN_DB} dB due to high THD.")
                    else:
                        print("Failed to adjust gain based on THD.")
            else:
                debug_print("THD within acceptable limits.")
        else:
            debug_print("SPL below THD calculation threshold. Skipping THD check.")

        # Determine the noise trend
        if rms > NOISE_THRESHOLD_HIGH:
            CURRENT_TREND = 1
        elif rms < NOISE_THRESHOLD_LOW:
            CURRENT_TREND = -1
        else:
            CURRENT_TREND = 0

        debug_print(f"Current trend: {CURRENT_TREND}")

        if CURRENT_TREND != 0:
            if CURRENT_TREND == PREVIOUS_TREND:
                TREND_COUNT += 1
            else:
                TREND_COUNT = 1
                PREVIOUS_TREND = CURRENT_TREND
        else:
            TREND_COUNT = 0

        debug_print(f"Trend count: {TREND_COUNT}")

        current_gain_db = get_gain_db(MICROPHONE_NAME)

        if current_gain_db is None:
            print("Failed to get current gain level. Retrying in 1 minute...")
            time.sleep(60)
            continue

        debug_print(f"Current gain: {current_gain_db} dB")

        if TREND_COUNT >= TREND_COUNT_THRESHOLD:
            if CURRENT_TREND == 1:
                # Decrease gain by DECREASE_GAIN_STEP_DB dB
                NEW_GAIN_DB = current_gain_db - DECREASE_GAIN_STEP_DB
                if NEW_GAIN_DB < MIN_GAIN_DB:
                    NEW_GAIN_DB = MIN_GAIN_DB
                success = set_gain_db(MICROPHONE_NAME, NEW_GAIN_DB)
                if success:
                    print(f"Background noise high. Decreased gain to {NEW_GAIN_DB} dB")
                    debug_print(f"Gain decreased to {NEW_GAIN_DB} dB due to high noise.")
                else:
                    print("Failed to decrease gain.")
            elif CURRENT_TREND == -1:
                # Increase gain by INCREASE_GAIN_STEP_DB dB
                NEW_GAIN_DB = current_gain_db + INCREASE_GAIN_STEP_DB
                if NEW_GAIN_DB > MAX_GAIN_DB:
                    NEW_GAIN_DB = MAX_GAIN_DB
                success = set_gain_db(MICROPHONE_NAME, NEW_GAIN_DB)
                if success:
                    print(f"Background noise low. Increased gain to {NEW_GAIN_DB} dB")
                    debug_print(f"Gain increased to {NEW_GAIN_DB} dB due to low noise.")
                else:
                    print("Failed to increase gain.")
            TREND_COUNT = 0
        else:
            debug_print("No gain adjustment needed based on noise trend.")

        # Sleep for 1 minute before the next iteration
        time.sleep(60)


if __name__ == "__main__":
    main()