Exponential moving average filter

DIRECTORY.md

@@ -1,6 +1,7 @@
 
 ## Audio Filters
   * [Butterworth Filter](audio_filters/butterworth_filter.py)
+  * [Ema Filter](audio_filters/ema_filter.py)
   * [Iir Filter](audio_filters/iir_filter.py)
   * [Show Response](audio_filters/show_response.py)
 

audio_filters/ema_filter.py

@@ -0,0 +1,71 @@
+"""
+By @Shreya123714
+
+https://en.wikipedia.org/wiki/Moving_average
+"""
+
+import numpy as np
+
+
+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 = 0.0
+
+    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],
+        ...              rtol=1e-5, atol=1e-8)
+        True
+        """
+        if not audio_signal:
+            return np.array([])
+
+        ema_signal: list[float] = []
+        self.ema_value = audio_signal[0]
+        ema_signal.append(self.ema_value)
+
+        for sample in audio_signal[1:]:
+            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()