diff --git a/schainpy/Processing/Noise.py b/schainpy/Processing/Noise.py
new file mode 100644
index 0000000..c3d042f
--- /dev/null
+++ b/schainpy/Processing/Noise.py
@@ -0,0 +1,108 @@
+import numpy
+
+from Model.JROHeader import *
+from Model.Voltage import Voltage
+
+def hildebrand_sekhon(Data, navg=1 ):
+    """
+    This method is for the objective determination of de noise level in Doppler spectra. This 
+    implementation technique is based on the fact that the standard deviation of the spectral 
+    densities is equal to the mean spectral density for white Gaussian noise
+    
+    Inputs:
+        Data    :    heights
+        navg    :    numbers of averages
+        
+    Return:
+        -1        :    any error
+        anoise    :    noise's level
+    """
+    divisor = 8
+    ratio = 7 / divisor
+    data = Data.reshape(-1)
+    npts = data.size #numbers of points of the data
+    
+    if npts < 32:
+        print "error in noise - requires at least 32 points"
+        return -1.0
+    
+    # data sorted in ascending order
+    nmin = int(npts/divisor + ratio);
+    s = 0.0
+    s2 = 0.0
+    data2 = data[:npts] 
+    data2.sort()
+    
+    for i in range(nmin):
+        s  += data2[i]
+        s2 += data2[i]**2;
+          
+    icount = nmin
+    iflag = 0 
+    
+    for i in range(nmin, npts):
+        s  += data2[i];
+        s2 += data2[i]**2
+        icount=icount+1;
+        p  = s / float(icount);
+        p2 = p**2;
+        q  = s2 / float(icount) - p2;
+        leftc = p2;
+        rightc = q * float(navg);
+
+        if leftc > rightc: 
+            iflag = 1; #No weather signal
+        # Signal detect: R2 < 1 (R2 = leftc/rightc)
+        if(leftc < rightc):
+            if iflag:
+                break
+
+    anoise = 0.0;
+    for j in range(i):
+        anoise += data2[j];
+
+    anoise = anoise / float(i);
+
+    return anoise;
+
+
+class Noise():
+    """
+    Clase que implementa los metodos necesarios para deternimar el nivel de ruido en un Spectro Doppler
+    """
+    m_DataObj = None
+
+
+    def __init__(self, m_Voltage=None):
+        """
+        Inicializador de la clase Noise para la la determinacion del nivel de ruido en un Spectro Doppler.
+
+        Affected:
+            self.m_DataObj
+        
+        Return:
+            None
+        """
+#        if m_Voltage == None:
+#            m_Voltage = Voltage()
+#        
+#        if not(isinstance(m_Voltage, Voltage)):
+#            raise ValueError, "in Noise class, m_Voltage must be an Voltage class object"
+        
+        self.m_DataObj = m_Voltage
+        
+        
+    def getNoiseLevelByHildebrandSekhon(self):
+        """
+        Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
+        
+        Return:
+            noise level
+        """
+        data = self.m_DataObj.data # heights x perfiles
+        #heights = numpy.transpose( Data, (2,0,1) ) # channel x profile x height
+        #data = Data[0,0,:]
+
+        noiselevel = hildebrand_sekhon(data)
+        
+        print noiselevel