Neural Network-Assisted FIR Filter Design: A Practical Fusion of Classical DSP and Machine Learning

This article introduces an innovative open-source project that combines artificial neural networks with traditional digital signal processing (DSP). It predicts filter parameters via neural networks and then uses SciPy to generate final coefficients, enabling an intelligent FIR filter design workflow. The project adopts a hybrid workflow that leverages the learning capabilities of neural networks while retaining the reliability of classical DSP methods, providing a new path for automated FIR filter design.

FIR filters are widely used in audio processing, communication systems, and other fields. Traditional design requires repeated iterations to adjust mutually constrained parameters such as cutoff frequency and transition bandwidth. The core innovation of this project is a hybrid workflow: users provide specification requirements, the neural network predicts optimized parameter adjustment values, and then the mature SciPy library's firwin function generates final coefficients, balancing learning ability and reliability.

The project adopts a modular design:

1. Data Generation: dataset_generator.py generates 50,000 samples covering regular to extreme scenarios;
2. Neural Network Model: ParamNet in hybrid_model.py accepts inputs [fc, trans, Rp, As, order, type] and uses a dual-head design to predict parameters;
3. Training and Inference: hybrid_train.py supports difficulty-weighted training, and the model is saved to a specified path;
4. Evaluation and Visualization: evaluate_model.py performs multi-dimensional evaluation, and streamlit_app.py provides an interactive web interface.

1. Hybrid Intelligent Paradigm: AI acts as an intelligent assistant to predict parameters instead of directly generating coefficients, ensuring result reliability;
2. Comprehensive Evaluation System: Covers multi-dimensional metrics such as regression loss, parameter MAE, and filter similarity;
3. Engineering Implementation: Includes complete type hints, modular architecture, web interface, multi-format export (TXT, Python, C, MATLAB), etc., with practical application value.

The project can accelerate engineers' design processes and reduce debugging times, especially suitable for scenarios involving complex specification trade-offs. It supports input of normalized frequency (fs=1.0) and actual frequency (e.g., 700Hz under 44.1kHz), covering both academic research and industrial application scenarios.

This project demonstrates the trend of AI enhancing traditional engineering—AI does not replace traditional methods but works in collaboration. This hybrid intelligent paradigm can be extended to other parameter tuning engineering problems. For developers, the project provides clear problem definition, reasonable architecture, comprehensive evaluation, and practical implementation, making it a good reference for learning the integration of ML and traditional engineering.