implement an lms filter from scratch to perform active noise cancellation from a live audio feed in python. make the code project grade in python

To implement an LMS (Least Mean Squares) filter for active noise cancellation from a live audio feed in Python, you will need to follow these steps:

  1. Import the necessary libraries:
main.py
import numpy as np
import sounddevice as sd

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  1. Define the LMSFilter class, which will contain the functions for noise cancellation:
main.py
class LMSFilter:
    def __init__(self, order, mu):
        self.order = order  # Filter order
        self.mu = mu  # Step size

        # Initialize filter coefficients to random values between -1 and 1
        self.weights = np.random.uniform(-1.0, 1.0, size=(order + 1,))

        # Initialize a buffer to store past input samples
        self.buffer = np.zeros(order + 1)

    def update(self, input_signal, noise_signal):
        # Update the buffer by shifting previous samples and adding the new input
        self.buffer = np.roll(self.buffer, 1)
        self.buffer[0] = input_signal

        # Calculate the filter output
        output_signal = np.dot(self.weights, self.buffer)

        # Compute the error signal
        error_signal = noise_signal - output_signal

        # Calculate the weight update
        weight_update = self.mu * error_signal * self.buffer

        # Update the filter coefficients
        self.weights += weight_update

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  1. Create an instance of the LMSFilter class:
main.py
filter_order = 128  # Set the filter order
step_size = 0.01  # Set the step size

noise_canceler = LMSFilter(filter_order, step_size)

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  1. Define a callback function to be called whenever a new audio buffer is available:
main.py
def audio_callback(indata, outdata, frames, time, status):
    # Perform active noise cancellation
    noise_canceler.update(indata[:, 0], indata[:, 1])

    # Output the filtered audio
    outdata[:, 0] = indata[:, 0] - noise_canceler.weights[0] * indata[:, 1]
    outdata[:, 1] = indata[:, 1] - noise_canceler.weights[0] * indata[:, 1]

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  1. Open a stream to capture and play audio using the sounddevice library:
main.py
with sd.Stream(callback=audio_callback):
    sd.sleep(10000)  # Run active noise cancellation for 10 seconds

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  1. Run the Python script and enjoy the active noise cancellation functionality. Adjust the filter order and step size values to achieve desired performance.

Please note that this implementation provides a basic demonstration of active noise cancellation and may require further improvements depending on the specific use case. It is also advisable to wrap the code in exception handling statements and further optimize the filter implementation for real-time audio processing if necessary.

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related categories

python
signal-processing
audio-processing
active-noise-cancellation
machine-learning

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