diff --git a/birdnet-pi/rootfs/helpers/spectrum_analysis.py b/birdnet-pi/rootfs/helpers/spectrum_analysis.py
new file mode 100644
index 000000000..1539f0ba3
--- /dev/null
+++ b/birdnet-pi/rootfs/helpers/spectrum_analysis.py
@@ -0,0 +1,59 @@
+import numpy as np
+import scipy.io.wavfile as wavfile
+import matplotlib.pyplot as plt
+import os
+import glob
+
+# Define the directory containing the WAV files
+input_directory = '/tmp/StreamData'
+output_directory = '/config'
+
+# Ensure the output directory exists
+if not os.path.exists(output_directory):
+    os.makedirs(output_directory)
+
+# Get a list of all WAV files in the input directory
+wav_files = glob.glob(os.path.join(input_directory, '*.wav'))
+
+# Process each file
+for file_path in wav_files:
+    # Load the WAV file
+    sample_rate, audio_data = wavfile.read(file_path)
+
+    # If stereo, select only one channel
+    if len(audio_data.shape) > 1:
+        audio_data = audio_data[:, 0]
+
+    # Apply the Hamming window to the audio data
+    hamming_window = np.hamming(len(audio_data))
+    windowed_data = audio_data * hamming_window
+
+    # Compute the FFT of the windowed audio data
+    audio_fft = np.fft.fft(windowed_data)
+    audio_fft = np.abs(audio_fft)
+
+    # Compute the frequencies associated with the FFT values
+    frequencies = np.fft.fftfreq(len(windowed_data), d=1/sample_rate)
+
+    # Select the range of interest
+    idx = np.where((frequencies >= 150) & (frequencies <= 15000))
+
+    # Calculate the saturation threshold based on the bit depth
+    bit_depth = audio_data.dtype.itemsize * 8
+    max_amplitude = 2**(bit_depth - 1) - 1
+    saturation_threshold = 0.8 * max_amplitude
+
+    # Plot the spectrum with a logarithmic Y-axis
+    plt.figure(figsize=(10, 6))
+    plt.semilogy(frequencies[idx], audio_fft[idx], label='Spectrum')
+    plt.axhline(y=saturation_threshold, color='r', linestyle='--', label='Saturation Threshold')
+    plt.xlabel("Frequency (Hz)")
+    plt.ylabel("Amplitude (Logarithmic)")
+    plt.title(f"Frequency Spectrum (150 - 15000 Hz) - {os.path.basename(file_path)}")
+    plt.legend()
+    plt.grid(True)
+
+    # Save the plot as a PNG file
+    output_filename = os.path.basename(file_path).replace('.wav', '_spectrum.png')
+    plt.savefig(os.path.join(output_directory, output_filename))
+    plt.close()  # Close the figure to free memory