# Audio Setup Guide for Fedora Core 41
# Audio Setup Guide for Fedora Core 41
This guide will help you set up a professional audio system on Fedora Core 41, similar to what you would find in Ubuntu Studio. It focuses on configuring JACK audio server with low-latency performance for audio processing, supporting capabilities for biometric voice analysis and integration with the Quantum Vacuum Propulsion Initiative (QVPI).
## Installing JACK Audio Connection Kit
JACK is a professional audio server that allows for routing audio between applications with low latency.
1. **Install JACK and related utilities**:
```bash
sudo dnf install jack-audio-connection-kit qjackctl pulseaudio-module-jack cadence ardour audacity hydrogen lmms
```
2. **Install additional audio processing tools**:
```bash
sudo dnf install sox ecasound ladspa-swh-plugins calf-plugins zita-at1 zita-rev1 rnnoise eq10q fil-plugins
```
3. **Install audio development libraries**:
```bash
sudo dnf install jack-audio-connection-kit-devel alsa-lib-devel portaudio-devel fftw-devel
```
## Configuring Real-Time Kernel for Low Latency
For optimal audio performance, you'll need a real-time kernel with low latency.
1. **Install the real-time kernel**:
```bash
sudo dnf install kernel-rt kernel-rt-devel
```
2. **Configure system limits for real-time audio processing**:
```bash
sudo nano /etc/security/limits.d/99-audio.conf
```
3. **Add the following lines to the file**:
```
@audio - rtprio 99
@audio - memlock unlimited
@audio - nice -19
```
4. **Add your user to the audio group**:
```bash
sudo usermod -a -G audio $USER
```
5. **Reboot your system**:
```bash
sudo reboot
```
## Configuring JACK Audio Server
1. **Launch QJackCtl**:
```bash
qjackctl &
```
2. **Configure JACK settings**:
- Click on "Setup"
- Set "Driver" to "ALSA"
- Set "Interface" to your audio device
- Set "Sample Rate" to 48000
- Set "Frames/Period" to 128 (smaller values reduce latency but increase CPU usage)
- Set "Periods/Buffer" to 2
- Enable "Force 16bit" if your hardware requires it
- Check "Realtime"
- Click "OK"
3. **Start JACK server**:
- Click "Start" in QJackCtl
## Integrating PulseAudio with JACK
To ensure all applications can route audio through JACK:
1. **Configure PulseAudio to work with JACK**:
```bash
sudo nano /etc/pulse/default.pa
```
2. **Find the line with `load-module module-udev-detect` and change it to**:
```
load-module module-udev-detect tsched=0
```
3. **Add the following lines at the end of the file**:
```
### Enable JACK modules
load-module module-jack-sink channels=2
load-module module-jack-source channels=2
set-default-sink jack_out
set-default-source jack_in
```
4. **Restart PulseAudio**:
```bash
pulseaudio -k && pulseaudio --start
```
## Installing Professional Audio Software
1. **Digital Audio Workstation (DAW)**:
```bash
sudo dnf install ardour
```
2. **Audio Editors**:
```bash
sudo dnf install audacity mhwaveedit
```
3. **MIDI Sequencer and Drum Machine**:
```bash
sudo dnf install rosegarden qtractor hydrogen
```
4. **Audio Effects and Plugins**:
```bash
sudo dnf install calf-plugins ladspa-*-plugins lv2-*-plugins
```
5. **Audio Visualization Tools**:
```bash
sudo dnf install baudline sonic-visualiser
```
## Setting Up Acoustic Levitation Capabilities
For the QVPI project's acoustic levitation component:
1. **Install scientific computing libraries**:
```bash
sudo dnf install python3-numpy python3-scipy python3-matplotlib
```
2. **Install ultrasonic transducer control libraries**:
```bash
sudo dnf install libusb-devel python3-pyusb
```
3. **Create a Python script for acoustic array control**:
```bash
mkdir -p ~/qvpi/acoustic
nano ~/qvpi/acoustic/transducer_array.py
```
4. **Add the following code to the script**:
```python
#!/usr/bin/env python3
"""
QVPI Acoustic Levitation Control System
This script controls an array of ultrasonic transducers to create
acoustic standing waves for particle levitation.
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
import time
# Configuration
NUM_TRANSDUCERS = 64 # Number of transducers in the array
FREQUENCY = 40000 # 40 kHz ultrasonic frequency
SAMPLING_RATE = 250000 # 250 kHz sampling rate
AMPLITUDE = 1.0 # Maximum amplitude
def generate_phase_pattern(pattern_type='focus', focal_point=(0, 0, 0.1)):
"""Generate phase patterns for different acoustic field configurations"""
phases = np.zeros(NUM_TRANSDUCERS)
# Transducer positions in a square array
size = int(np.sqrt(NUM_TRANSDUCERS))
spacing = 0.01 # 1cm spacing between transducers
positions = np.zeros((NUM_TRANSDUCERS, 3))
for i in range(size):
for j in range(size):
idx = i * size + j
positions[idx] = [i*spacing - (size/2)*spacing,
j*spacing - (size/2)*spacing,
0]
if pattern_type == 'focus':
# Calculate phases to focus at focal_point
for i in range(NUM_TRANSDUCERS):
distance = np.linalg.norm(positions[i] - np.array(focal_point))
wavelength = 343 / FREQUENCY # speed of sound / frequency
phases[i] = (2 * np.pi * (distance % wavelength)) / wavelength
elif pattern_type == 'vortex':
# Create acoustic vortex beam with orbital angular momentum
center = np.mean(positions[:, :2], axis=0)
for i in range(NUM_TRANSDUCERS):
x, y = positions[i, :2] - center
angle = np.arctan2(y, x)
phases[i] = angle # Creates a phase winding of 2Ï€
elif pattern_type == 'standing_wave':
# Create a standing wave pattern
for i in range(NUM_TRANSDUCERS):
phases[i] = np.pi * (positions[i, 0] > 0) # Half in-phase, half out-of-phase
return phases, positions
def simulate_acoustic_field(phases, positions, resolution=50):
"""Simulate the acoustic pressure field in a 2D plane"""
x = np.linspace(-0.1, 0.1, resolution)
y = np.linspace(-0.1, 0.1, resolution)
z = 0.05 # Fixed z-plane for visualization
X, Y = np.meshgrid(x, y)
pressure = np.zeros((resolution, resolution), dtype=complex)
k = 2 * np.pi * FREQUENCY / 343 # Wave number
for i in range(NUM_TRANSDUCERS):
xi, yi, zi = positions[i]
phase = phases[i]
# Calculate distance from each transducer to each point in the field
R = np.sqrt((X - xi)**2 + (Y - yi)**2 + z**2)
# Acoustic pressure contribution (simplified model)
contribution = np.exp(1j * (k * R + phase)) / R
pressure += contribution
return X, Y, np.abs(pressure)**2
def visualize_field(X, Y, field):
"""Visualize the acoustic pressure field"""
plt.figure(figsize=(10, 8))
plt.pcolormesh(X, Y, field, shading='auto', cmap='viridis')
plt.colorbar(label='Pressure Intensity')
plt.title('Acoustic Pressure Field')
plt.xlabel('X (meters)')
plt.ylabel('Y (meters)')
plt.axis('equal')
plt.tight_layout()
plt.savefig('acoustic_field.png')
plt.show()
def main():
"""Main function to demonstrate acoustic field control"""
print("QVPI Acoustic Levitation Simulation")
print("1. Focused Field")
print("2. Vortex Beam")
print("3. Standing Wave")
choice = input("Select pattern type (1-3): ")
if choice == '1':
pattern = 'focus'
elif choice == '2':
pattern = 'vortex'
else:
pattern = 'standing_wave'
phases, positions = generate_phase_pattern(pattern)
X, Y, field = simulate_acoustic_field(phases, positions)
visualize_field(X, Y, field)
print(f"Acoustic field pattern '{pattern}' generated and visualized")
print("This simulation demonstrates the acoustic field configuration")
print("that could be used in the QVPI vessel for acoustic levitation.")
if __name__ == "__main__":
main()
```
5. **Make the script executable**:
```bash
chmod +x ~/qvpi/acoustic/transducer_array.py
```
## Advanced Audio Monitoring for QVPI
To monitor and analyze audio signals from the QVPI:
1. **Install audio analysis tools**:
```bash
sudo dnf install python3-librosa python3-sounddevice python3-pyaudio
```
2. **Create a real-time audio analysis script**:
```bash
nano ~/qvpi/acoustic/audio_analyzer.py
```
3. **Add the following code**:
```python
#!/usr/bin/env python3
"""
QVPI Audio Monitoring and Analysis System
Real-time analysis of acoustic signatures from the QVPI system.
"""
import numpy as np
import matplotlib.pyplot as plt
import sounddevice as sd
import scipy.signal as signal
from threading import Thread
import time
import queue
# Audio settings
SAMPLE_RATE = 48000
BLOCK_SIZE = 2048
CHANNELS = 1
Q = queue.Queue()
# FFT settings
FREQ_MIN = 20
FREQ_MAX = 20000
FFT_SIZE = 4096
# Initialize plot
plt.ion()
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
line1, = ax1.plot(np.zeros(BLOCK_SIZE))
line2, = ax2.plot(np.zeros(FFT_SIZE//2))
# Set up plot parameters
ax1.set_ylim(-1, 1)
ax1.set_xlim(0, BLOCK_SIZE)
ax1.set_title('Waveform')
ax1.set_xlabel('Samples')
ax1.set_ylabel('Amplitude')
freq_scale = np.linspace(0, SAMPLE_RATE/2, FFT_SIZE//2)
ax2.set_ylim(0, 100)
ax2.set_xlim(FREQ_MIN, FREQ_MAX)
ax2.set_xscale('log')
ax2.set_title('Frequency Spectrum')
ax2.set_xlabel('Frequency (Hz)')
ax2.set_ylabel('Magnitude (dB)')
ax2.grid(True)
# Resonance detection parameters
TARGET_RESONANCES = [432, 528, 639, 741, 852, 963]
RESONANCE_TOLERANCE = 5 # Hz
resonance_markers = []
for freq in TARGET_RESONANCES:
marker, = ax2.plot([freq, freq], [0, 100], 'r--', alpha=0.5)
resonance_markers.append(marker)
ax2.text(freq, 90, f'{freq} Hz', rotation=90, verticalalignment='top')
plt.tight_layout()
def audio_callback(indata, frames, time, status):
"""Callback for audio input stream"""
if status:
print(f"Status: {status}")
Q.put(indata.copy())
def update_plot():
"""Update the plot with new audio data"""
while True:
try:
# Get data from queue
data = Q.get(block=False)
# Update time domain plot
samples = data[:, 0]
line1.set_ydata(samples)
# Calculate FFT
windowed = samples * signal.windows.hann(len(samples))
spectrum = np.abs(np.fft.rfft(windowed, n=FFT_SIZE))
spectrum_db = 20 * np.log10(spectrum + 1e-10)
# Update frequency domain plot
line2.set_ydata(spectrum_db[:FFT_SIZE//2])
# Detect resonances
detect_resonances(spectrum_db[:FFT_SIZE//2], freq_scale)
# Redraw plots
fig.canvas.draw_idle()
fig.canvas.flush_events()
except queue.Empty:
time.sleep(0.01)
continue
def detect_resonances(spectrum, frequencies):
"""Detect and highlight resonant frequencies"""
# Find peaks in the spectrum
peaks, _ = signal.find_peaks(spectrum, height=50, distance=20)
peak_freqs = frequencies[peaks]
peak_mags = spectrum[peaks]
# Check if any peaks are near our target resonances
for i, target in enumerate(TARGET_RESONANCES):
matches = np.where(np.abs(peak_freqs - target) < RESONANCE_TOLERANCE)[0]
if len(matches) > 0:
closest = matches[np.argmin(np.abs(peak_freqs[matches] - target))]
resonance_markers[i].set_color('r')
resonance_markers[i].set_alpha(1.0)
else:
resonance_markers[i].set_color('r')
resonance_markers[i].set_alpha(0.3)
def main():
"""Main function to start audio monitoring"""
print("QVPI Audio Monitoring System")
print("Starting real-time audio analysis...")
# Start audio stream
stream = sd.InputStream(
samplerate=SAMPLE_RATE,
blocksize=BLOCK_SIZE,
channels=CHANNELS,
callback=audio_callback
)
# Start plot update thread
plot_thread = Thread(target=update_plot)
plot_thread.daemon = True
plot_thread.start()
# Start streaming
with stream:
print("Audio monitoring started. Press Ctrl+C to stop.")
try:
while True:
time.sleep(0.1)
except KeyboardInterrupt:
print("Audio monitoring stopped.")
if __name__ == "__main__":
main()
```
4. **Make the script executable**:
```bash
chmod +x ~/qvpi/acoustic/audio_analyzer.py
```
## Merkaba Acoustic Resonance Configuration
To implement the Merkaba-inspired resonant acoustic cavity setup:
1. **Create a directory for the Merkaba configuration**:
```bash
mkdir -p ~/qvpi/merkaba
```
2. **Create a configuration file**:
```bash
nano ~/qvpi/merkaba/resonance_config.py
```
3. **Add the following code**:
```python
#!/usr/bin/env python3
"""
QVPI Merkaba Acoustic Resonance Configuration
This script calculates optimal resonant frequencies for a Merkaba-shaped cavity.
"""
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Constants
SPEED_OF_SOUND = 343 # m/s
AIR_DENSITY = 1.2 # kg/m³
class MerkabaResonator:
def __init__(self, size=1.0):
"""Initialize Merkaba resonator with given size (in meters)"""
self.size = size
self.vertices = self._generate_merkaba_vertices(size)
self.resonant_modes = []
def _generate_merkaba_vertices(self, size):
"""Generate vertices for Merkaba geometry (two interlocked tetrahedra)"""
# First tetrahedron (pointing up)
tetra1 = np.array([
[0, 0, size/2], # Top vertex
[size/2, 0, -size/6], # Bottom right
[-size/4, size*np.sqrt(3)/4, -size/6], # Bottom left
[-size/4, -size*np.sqrt(3)/4, -size/6] # Bottom back
])
# Second tetrahedron (pointing down)
tetra2 = np.array([
[0, 0, -size/2], # Bottom vertex
[size/2, 0, size/6], # Top right
[-size/4, size*np.sqrt(3)/4, size/6], # Top left
[-size/4, -size*np.sqrt(3)/4, size/6] # Top back
])
return {'up': tetra1, 'down': tetra2}
def calculate_resonant_frequencies(self, max_n=5):
"""Calculate resonant frequencies for Merkaba cavity"""
# Approximation: use inscribed sphere and calculate modes
# Real calculation would require solving wave equation in complex geometry
# Estimate effective radius as average distance from center to vertices
distances = []
for direction in ['up', 'down']:
for vertex in self.vertices[direction]:
distances.append(np.linalg.norm(vertex))
effective_radius = np.mean(distances)
# Calculate spherical cavity modes (approximation)
modes = []
for n in range(1, max_n + 1):
for l in range(n):
for m in range(-l, l + 1):
# Zeros of spherical Bessel functions approximation
# In reality, would need to solve for exact zeros
k = np.pi * n / effective_radius
freq = SPEED_OF_SOUND * k / (2 * np.pi)
# Add mode information
modes.append({
'frequency': freq,
'n': n, 'l': l, 'm': m,
'description': f"Mode ({n},{l},{m}): {freq:.2f} Hz"
})
# Sort by frequency
self.resonant_modes = sorted(modes, key=lambda x: x['frequency'])
return self.resonant_modes
def sacred_geometry_frequencies(self):
"""Calculate frequencies associated with sacred geometry ratios"""
# Base frequency (fundamental)
base = 432 # Hz - associated with A=432Hz tuning
# Frequency ratios based on sacred geometry
ratios = {
'1:1': 1.0, # Fundamental
'phi': (1 + np.sqrt(5))/2, # Golden ratio
'sqrt(2)': np.sqrt(2), # Octahedron diagonal ratio
'sqrt(3)': np.sqrt(3), # Tetrahedron height ratio
'3:2': 3/2, # Perfect fifth
'4:3': 4/3, # Perfect fourth
'9:8': 9/8, # Major second
'16:9': 16/9 # Pythagorean major seventh
}
# Calculate frequencies
frequencies = {}
for name, ratio in ratios.items():
frequencies[name] = base * ratio
return frequencies
def visualize_merkaba(self):
"""Visualize the Merkaba geometry with resonant nodes"""
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
# Plot first tetrahedron
tetra1 = self.vertices['up']
faces1 = [
[tetra1[0], tetra1[1], tetra1[2]],
[tetra1[0], tetra1[2], tetra1[3]],
[tetra1[0], tetra1[3], tetra1[1]],
[tetra1[1], tetra1[2], tetra1[3]]
]
for face in faces1:
face = np.array(face)
ax.plot_trisurf(face[:,0], face[:,1], face[:,2], alpha=0.3, color='blue')
# Plot second tetrahedron
tetra2 = self.vertices['down']
faces2 = [
[tetra2[0], tetra2[1], tetra2[2]],
[tetra2[0], tetra2[2], tetra2[3]],
[tetra2[0], tetra2[3], tetra2[1]],
[tetra2[1], tetra2[2], tetra2[3]]
]
for face in faces2:
face = np.array(face)
ax.plot_trisurf(face[:,0], face[:,1], face[:,2], alpha=0.3, color='red')
# Show resonant nodes if calculated
if self.resonant_modes:
# Only plot first few modes
for mode in self.resonant_modes[:5]:
n, l, m = mode['n'], mode['l'], mode['m']
# Simplified visualization - just place markers at estimated positions
r = self.size/2 * (0.3 + 0.1 * n)
theta = np.pi * l/5
phi = np.pi * (m + l) / (2*l + 1) if l != 0 else 0
x = r * np.sin(theta) * np.cos(phi)
y = r * np.sin(theta) * np.sin(phi)
z = r * np.cos(theta)
ax.scatter([x], [y], [z], s=100, c='green',
label=f"{mode['frequency']:.0f} Hz")
# Set plot properties
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Merkaba Resonance Configuration')
# Equal aspect ratio
max_range = np.array([
np.max(tetra1[:,0])-np.min(tetra1[:,0]),
np.max(tetra1[:,1])-np.min(tetra1[:,1]),
np.max(tetra1[:,2])-np.min(tetra1[:,2])
]).max() / 2.0
mid_x = (np.max(tetra1[:,0])+np.min(tetra1[:,0])) * 0.5
mid_y = (np.max(tetra1[:,1])+np.min(tetra1[:,1])) * 0.5
mid_z = (np.max(tetra1[:,2])+np.min(tetra1[:,2])) * 0.5
ax.set_xlim(mid_x - max_range, mid_x + max_range)
ax.set_ylim(mid_y - max_range, mid_y + max_range)
ax.set_zlim(mid_z - max_range, mid_z + max_range)
plt.legend()
plt.tight_layout()
plt.savefig('merkaba_resonance.png')
plt.show()
def main():
"""Main function to demonstrate Merkaba resonance calculation"""
print("QVPI Merkaba Acoustic Resonance Calculator")
# Create Merkaba resonator
size = float(input("Enter Merkaba size in meters (default: 0.5): ") or "0.5")
merkaba = MerkabaResonator(size)
# Calculate resonant frequencies
modes = merkaba.calculate_resonant_frequencies(max_n=5)
# Print resonant modes
print(" Resonant Modes:")
for i, mode in enumerate(modes[:10]):
print(f"{i+1}. {mode['description']}")
# Print sacred geometry frequencies
sacred_freqs = merkaba.sacred_geometry_frequencies()
print(" Sacred Geometry Frequencies:")
for name, freq in sacred_freqs.items():
print(f"{name}: {freq:.2f} Hz")
# Visualize Merkaba
visualize = input(" Visualize Merkaba resonator? (y/n): ").lower() == 'y'
if visualize:
merkaba.visualize_merkaba()
if __name__ == "__main__":
main()
```
4. **Make the script executable**:
```bash
chmod +x ~/qvpi/merkaba/resonance_config.py
```
## Testing Your Audio Setup
To verify your audio setup is working correctly:
1. **Test JACK Configuration**:
```bash
qjackctl &
```
- Start the JACK server by clicking "Start"
- Open the "Connections" window to route audio between applications
2. **Test Audio Recording and Playback**:
```bash
audacity &
```
- Create a new audio track
- Record a short audio clip
- Play it back to ensure the system is working
3. **Test the Acoustic Simulation**:
```bash
cd ~/qvpi/acoustic/
./transducer_array.py
```
4. **Test the Audio Analyzer**:
```bash
cd ~/qvpi/acoustic/
./audio_analyzer.py
```
5. **Test the Merkaba Resonance Calculator**:
```bash
cd ~/qvpi/merkaba/
./resonance_config.py
```
## Troubleshooting
If you encounter issues with your audio setup:
1. **JACK Won't Start**:
- Check that your audio device is properly connected
- Ensure your user is in the audio group: `groups $USER`
- Try setting a higher frames/period value in QJackCtl setup
- Verify your system limits: `ulimit -r -l`
2. **Audio Dropouts or Xruns**:
- Increase the frames/period value in QJackCtl
- Close CPU-intensive applications
- Disable power management for better performance: `sudo cpupower frequency-set -g performance`
3. **No Sound in Applications**:
- Check that applications are correctly routed in QJackCtl connections
- Verify PulseAudio is properly connected to JACK
4. **PulseAudio Problems**:
- Reset PulseAudio: `pulseaudio -k && pulseaudio --start`
- Check the connection between PulseAudio and JACK in QJackCtl
## Conclusion
You now have a professional audio setup on Fedora Core 41 with capabilities for advanced audio processing, acoustic levitation simulation, and Merkaba resonance analysis. This setup provides the foundation for the audio components of the Quantum Vacuum Propulsion Initiative (QVPI) project.
For further optimization and integration with quantum cryptography components, refer to the quantum_crypto_setup.md guide.
