diff --git a/broadband.png b/broadband.png
deleted file mode 100644
index e3eea5e4..00000000
Binary files a/broadband.png and /dev/null differ
diff --git a/img/broadband.png b/img/broadband.png
new file mode 100644
index 00000000..11029572
Binary files /dev/null and b/img/broadband.png differ
diff --git a/img/psd.png b/img/psd.png
new file mode 100644
index 00000000..2be079f0
Binary files /dev/null and b/img/psd.png differ
diff --git a/noise_processing.py b/noise_processing.py
index 6fdd7db9..8d29a117 100644
--- a/noise_processing.py
+++ b/noise_processing.py
@@ -2,12 +2,15 @@
 from orcasound_noise.pipeline.pipeline import NoiseAnalysisPipeline
 from orcasound_noise.utils import Hydrophone
 import datetime as dt
+import os
 
 import pandas as pd
 import matplotlib.pyplot as plt
 from orcasound_noise.pipeline.acoustic_util import plot_spec, plot_bb
 
 
+import plotly.graph_objects as go
+
 #Example 1: Port Townsend, 1 Hz Frequency, 60-second samples
 if __name__ == '__main__':
     pipeline = NoiseAnalysisPipeline(Hydrophone.PORT_TOWNSEND,
@@ -25,8 +28,59 @@
 psd_df = pd.read_parquet(psd_path)
 bb_df = pd.read_parquet(broadband_path)
 
-plot_spec(psd_df)
-plot_bb(bb_df)
 
-plt.xticks(rotation=45)
-plt.savefig('broadband.png')
+# Create a new directory because it does not exist
+if not os.path.exists('img'):
+   os.makedirs('img')
+
+def plot_spec(psd_df):
+    """
+    This function converts a table of power spectral data, having the columns represent frequency bins and the rows
+    represent time segments, to a spectrogram.
+
+    Args:
+        psd_df: Dataframe of power spectral data.
+
+    Returns: Spectral plot
+    """
+
+    fig = go.Figure(
+        data=go.Heatmap(x=psd_df.index, y=psd_df.columns, z=psd_df.values.transpose(), colorscale='Viridis',
+                        colorbar={"title": 'Magnitude'}))
+    fig.update_layout(
+        title="Hydrophone Power Spectral Density",
+        xaxis_title="Time",
+        yaxis_title="Frequency (Hz)",
+        legend_title="Magnitude"
+    )
+    fig.update_yaxes(type="log")
+    fig.show()
+    return(fig)
+
+def plot_bb(bb_df):
+    """
+    This function plots the broadband levels in relative decibels.
+
+    Args:
+        bb_df: Dataframe of broadband levels.
+
+    Returns: Time series of broadband levels.
+    """
+    plt.figure()
+    plt.plot(bb_df)
+    plt.title('Relative Broadband Levels')
+    plt.xlabel('Time')
+    plt.ylabel('Relative Decibels')
+    plt.xticks(rotation = 45)
+    # plt.show()
+    return(plt.gcf())
+
+
+
+fig = plot_spec(psd_df)
+fig.write_image('img/psd.png')
+
+
+
+fig = plot_bb(bb_df)
+fig.savefig('img/broadband.png')