ParaFilt

ParaFilt is a Python package that provides a collection of parallel adaptive filter implementations for efficient signal processing applications. It leverages the power of parallel processing using PyTorch, enabling faster and scalable computations on multi-core CPUs and GPUs.

Features

• Parallel algorithm framework that allows computing iterative algorithms in a parallel way.
• Parallel implementation of popular adaptive filter algorithms, including LMS, NLMS, RLS, and more.
• Possibility for researchers to integrate their own adaptive filter algorithms for parallel computing.
• Comprehensive documentation and examples for quick start and usage guidance.

Installation

To install ParaFilt, you can use pip: pip install parafilt

Usage

Inputs:

desired_signal: (batch_size, samples) - Desired signal tensor.
input_signal: (samples) - Input tensor.

Returns:

Tuple containing the estimated output and the error signal (d_est, e).
d_est: (batch_size, samples) - Estimated output tensor.
e: (batch_size, samples) - Error signal tensor.

Here's an example of how to use the package to create and apply the LMS filter: ```python import parafilt

Create an instance of the LMS filter

lms_filter = parafilt.LMS(hop=1024, framelen=4096, filterlen=1024).cuda()

Perform parallel filter iteration

dest, e = lmsfilter(desiredsignal, inputsignal) ```

Here's an example of how to use the package to create and apply the RLS filter: ```python import parafilt

Create an instance of the RLS filter

rls_filter = parafilt.RLS(hop=1024, framelen=4096, filterlen=30).cuda()

Perform parallel filter iteration

dest, e = rlsfilter(desiredsignal, inputsignal) ```

For detailed usage example, please refer to this notebook.

Parallel Algorithm Framework

Parafilt provides a parallel algorithm framework that enables researchers to implement and execute iterative algorithms in a parallelized manner. This framework allows for efficient utilization of multi-core CPUs and GPUs, resulting in significant speedup for computationally intensive algorithms.

To leverage the parallel algorithm framework, researchers can extend the base classes provided by Parafilt and utilize the parallel computation capabilities provided by PyTorch.

Here's an example of how to use the package to create your own filter: ```python from parafilt import BaseFilter

class TemplateFilter(BaseFilter): def init(self, hop: int, framelen: int, filterlen: int = 1024, weightsdelay: Optional[int] = None, weightsrange: (float, float) = (-65535, 65535)): ''' Template filter class that extends the BaseFilter class. :param hop: Hop size for frame processing. :param framelen: Length of each frame. :param filterlen: Length of the filter. :param weightsdelay: Delay for the weights, If None, it is set to framelen-1 (default: None). :param weightsrange: Range for the filter weights (default: (-65535, 65535)). ''' super().init(hop=hop, framelen=framelen, filterlen=filterlen, weightsdelay=weightsdelay, weightsrange=weightsrange)

@torch.no_grad()
def forward_settings(self, d: torch.Tensor, x: torch.Tensor):
    '''
    Placeholder for the settings during forward.
    :param d: Desired signal tensor.
        Shape: (batch_size, frame_length)
    :param x: Input tensor.
        Shape: (1, frame_length, filter_length)
    '''
    return

@torch.no_grad()
def iterate(self, d: torch.Tensor, x: torch.Tensor) -> (torch.Tensor, torch.Tensor):
    '''
    Placeholder for the filter iteration.
    :param d: Desired signal tensor.
        Shape: (batch_size, frame_length)
    :param x: Input tensor.
        Shape: (1, frame_length, filter_length)
    :return:
        torch.Tensor: Estimated output tensor.
            Shape: (batch_size, frame_length)
        torch.Tensor: Error tensor.
            Shape: (batch_size, frame_length)
    '''
    raise NotImplementedError

```

Citation

If you use this software, please cite it as below.

@software{asaf_zorea_2023_8004059, author = {Asaf Zorea}, title = {ParaFilt: Parallel Adaptive Filtering for Efficient Signal Processing}, month = jun, year = 2023, publisher = {Zenodo}, version = {v0.1.2-beta}, doi = {10.5281/zenodo.8004059}, url = {https://doi.org/10.5281/zenodo.8004059} }

Future Work

• Implementation of CUDA code for the parallel frameworks and filter algorithms to achieve even faster computations.
• Addition of an option for zero-padding, enabling the output size to match the input size without discarding any samples during the frame decomposition and reconstruction process after performing the filter.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request on the GitHub repository.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Contact

For any inquiries or questions, please contact zoreasaf@gmail.com.