add code and stuff

gen_brownian.py

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+import argparse
+import numpy as np
+import seaborn as sns
+import soundfile as sf
+import matplotlib.pyplot as plt
+from scipy.signal import lfilter, butter, sosfilt
+
+def parse_arguments():
+    parser = argparse.ArgumentParser(description="Generate and process Brownian noise.")
+    parser.add_argument("--length", "-l", type=int, default=100, help="Length of the noise in seconds.")
+    parser.add_argument("--sample_rate", "-sr", type=int, default=44100, help="Sample rate in Hz. [44100, 48000, 96000, etc]")
+    parser.add_argument("--file_name", "-f", type=str, default="brownian_noise.wav", help="Output file name.")
+    parser.add_argument("--plot", "-p", action="store_true", help="Specify this flag to save a plot.")
+    parser.add_argument("--plot_file_name", "-pf", type=str, default="plot.png", help="File name for saving the plot if plot is enabled.")
+    parser.add_argument("--f3dB", "-f3", type=int, default=80, help="f3dB cutoff frequency (similar to high pass filter) in Hz")
+    parser.add_argument("--highpass", "-hp", dest="highpass", type=int, default=20, help="High pass filter cutoff frequency in Hz")
+    parser.add_argument("--no-highpass", "-nhp", dest="do_highpass", action="store_false", help="Disable high pass filtering.")
+    parser.set_defaults(highpass=True)
+    return parser.parse_args()
+
+def plot_audio_and_spectrum(audio, fs, file_name):
+    """Save the waveform and frequency spectrum of the audio."""
+    print("Plotting and saving audio and spectrum...")
+    sns.set(style="whitegrid")
+
+    n = len(audio)
+    freq = np.fft.rfftfreq(n, d=1/fs)
+    magnitude = np.abs(np.fft.rfft(audio)) / n
+    magnitude_db = 20 * np.log10(magnitude + 1e-12)  
+
+    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
+
+    # Plotting the waveform
+    time = np.linspace(0, len(audio) / fs, num=len(audio))
+    sns.lineplot(x=time, y=audio, ax=axes[0], color='b', linewidth=0.5)
+    axes[0].set_title('Waveform of Brownian Noise')
+    axes[0].set_xlabel('Time (seconds)')
+    axes[0].set_ylabel('Amplitude')
+
+    # Plotting the frequency spectrum
+    sns.lineplot(x=freq, y=magnitude_db, ax=axes[1], color='r', linewidth=0.5)
+    axes[1].set_title('Frequency Spectrum of Brownian Noise')
+    axes[1].set_xlabel('Frequency (Hz)')
+    axes[1].set_ylabel('Intensity (dB)')
+    axes[1].set_xscale('log')
+
+    plt.tight_layout()
+    plt.savefig(file_name, dpi=300)
+    plt.close()
+    print(f"Plot saved as {file_name}")
+
+def high_pass_filter(waveform, fs, hz=20):
+    print("Applying high pass filter...")
+    sos = butter(10, hz, 'hp', fs=fs, output='sos')
+    filtered_signal = sosfilt(sos, waveform)
+    return filtered_signal
+
+def find_alpha(Fs, f3dB):
+    """
+    Calculate the alpha value for the given cutoff frequency.
+
+    https://dsp.stackexchange.com/questions/40462/exponential-moving-average-cut-off-frequency/40465#40465
+    """
+    print(f"Calculating alpha for cutoff frequency {f3dB} Hz...")
+    return (
+            np.sqrt(
+                np.power(np.cos(2 * np.pi * f3dB / Fs), 2) 
+                - 4 * np.cos(2 * np.pi * f3dB / Fs) 
+                + 3
+            ) 
+            + np.cos(2 * np.pi * f3dB / Fs) 
+            - 1
+    )
+
+def generate_brownian_noise(N, alpha):
+    """
+    Generate brownian noise with the given alpha value.
+    
+    https://dsp.stackexchange.com/questions/40462/exponential-moving-average-cut-off-frequency/40465#40465
+    """
+    print("Generating Brownian noise...")
+    x = np.random.normal(0, 1, N) 
+    b = [alpha]
+    a = [1, -(1-alpha)]
+    y = lfilter(b, a, x)
+    return y
+
+def main():
+    args = parse_arguments()
+    Fs = args.sample_rate
+    target_f3dB = args.f3dB
+    alpha = find_alpha(Fs, target_f3dB)
+    print(f"Calculated alpha: {alpha}")
+
+    N = Fs * args.length
+    brownian_noise = generate_brownian_noise(N, alpha)
+
+    if args.do_highpass:
+        brownian_noise = high_pass_filter(brownian_noise, Fs, args.highpass)
+
+    max_value = np.max(abs(brownian_noise))
+    brownian_noise = (brownian_noise / max_value)
+    print(f"Brownian noise normalized to range [-1, 1].")
+
+    sf.write(args.file_name, brownian_noise*0.9, Fs, subtype='PCM_24')
+    print(f"Brownian noise WAV file '{args.file_name}' has been generated and saved.")
+
+    if args.plot:
+        plot_audio_and_spectrum(brownian_noise, Fs, args.plot_file_name)
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+numpy
+seaborn
+soundfile
+matplotlib
+scipy