diff --git a/audio_filters/ema_filter.py b/audio_filters/ema_filter.py
new file mode 100644
index 000000000..2786cd110
--- /dev/null
+++ b/audio_filters/ema_filter.py
@@ -0,0 +1,67 @@
+"""
+By @Shreya123714
+
+https://en.wikipedia.org/wiki/Moving_average
+"""
+
+import numpy as np
+from typing import List
+
+class EMAFilter:
+    """
+    A class for applying an Exponential Moving Average (EMA) filter
+    to audio data.
+    
+    Attributes:
+        alpha (float): Smoothing factor where 0 < alpha <= 1.
+        ema_value (float): Stores the most recent EMA value
+        for the ongoing calculation.
+    """
+
+    def __init__(self, alpha: float) -> None:
+        """
+        Initialize the Exponential Moving Average (EMA) filter.
+
+        Parameters:
+        alpha (float): Smoothing factor where 0 < alpha <= 1.
+        
+        Raises:
+            ValueError: If alpha is not within the range (0, 1].
+        """
+        if not (0 < alpha <= 1):
+            raise ValueError("Alpha must be between 0 and 1.")
+        self.alpha = alpha
+        self.ema_value = None
+
+    def apply(self, audio_signal: List[float]) -> np.ndarray:
+        """
+        Apply the EMA filter to a sequence of
+        audio signal values.
+
+        Parameters:
+        audio_signal (List[float]): List of numerical values
+        representing the audio signal.
+
+        Returns:
+        np.ndarray: Array containing the smoothed audio signal.
+
+        Example:
+        >>> ema_filter = EMAFilter(0.2)
+        >>> np.allclose(ema_filter.apply([0.1, 0.5, 0.8, 0.6, 0.3, 0.9, 0.4]), 
+        ...             [0.1, 0.18, 0.304, 0.3632, 0.35056, 0.460448, 0.4483584])
+        True
+        """
+        ema_signal = []
+        for sample in audio_signal:
+            if self.ema_value is None:
+                # Initialize the EMA with the first sample
+                self.ema_value = sample
+            else:
+                # Calculate the EMA for the current sample
+                self.ema_value = self.alpha * sample + (1 - self.alpha) * self.ema_value
+            ema_signal.append(self.ema_value)
+        return np.array(ema_signal)
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()