commit 5e7efdd7db56fef5ed6c2ff9b416a92a96bde4a3
Author: Matthew <matthew@pop-os.lan>
Date:   Sat Mar 22 10:31:35 2025 -0400

    Initial commit

diff --git a/concept_fig.png b/concept_fig.png
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Binary files /dev/null and b/concept_fig.png differ
diff --git a/sound_profile.py b/sound_profile.py
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+++ b/sound_profile.py
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+import matplotlib.pyplot as plt
+import numpy as np
+import random
+import acoustics.decibel as db
+
+
+freq_upper = 1000	#size of the frequency spectrum we want to investigate
+freq_div = 10
+
+x = np.array(range(freq_upper))	#initialize frequencies (x-axis)
+bkgd = np.zeros(freq_upper)
+source = np.zeros(freq_upper)
+
+for i in range(len(bkgd)):
+	bkgd[i] = np.log((x[i]+1)/freq_div)  #input model for background here. each freq is assigned an SNR
+	
+tonals = [20,40,100,500, 800]  	#build our threat object here
+widths = [2,2,2,2,2]		#how diffuse is each tonal
+decr = [.5,.5,.5,.95,.98]		#sound decrement from ambient
+
+
+for i in range(len(source)):
+	source[i] = random.randint(0,10)/200
+	
+	
+for tone,wide,loud in zip(tonals, widths, decr):		#right now the source is based on bkgd
+	freq = tone - wide//2				#strength, but we will need to just
+	while freq < tone + wide//2:			#assign values
+		source[freq] = bkgd[freq]*(1-loud)		#
+		freq += 1				#
+
+		
+received = db.dbadd(bkgd, source)	
+	
+	
+plt.plot(x/freq_div,received, label = "Legacy")
+plt.plot(x/freq_div,source, label = "Distilled")
+plt.xlabel("freq (Hz)")
+plt.ylabel("SNR")
+plt.title("Concept")
+
+plt.legend()
+plt.show()
+
+
+
+
+