Frequency-Selective Memory Networks for Adaptive Signal Processing - Source Code and Datasets

FSM-Net: Dual-Path Signal Classification with Learned Frequency Decomposition This repository provides the complete Python implementation and experimental framework for FSM-Net (Frequency-Selective Memory Network), a neural architecture that learns to decompose signals into task-relevant spectral components through end-to-end gradient optimization. The accompanying paper proposes a learnable STFT-based frequency gating mechanism, dual-pathway LSTMs with asymmetric memory dynamics, and an attention-based adaptive fusion module for time-series classification tasks. Contents FSM-Net.py: Single-file implementation (~1,600 lines) containing the full FSM-Net architecture, five baseline models (Standard LSTM, 1D-CNN, CNN-LSTM, Transformer, SincNet), dataset loaders, training/evaluation pipelines, ablation studies, loss-weight sensitivity analysis, noise robustness evaluation, and publication-quality figure generation (Figures 2–7 in the paper). Datasets: CWRU Bearing Fault Diagnosis dataset (12 kHz, 4-class), Paderborn University Bearing dataset (64 kHz, 3-class), and MIT-BIH Arrhythmia dataset (360 Hz, binary classification). Key Features Learnable frequency decomposition via trainable gating networks applied to STFT coefficients, discovering task-optimal spectral boundaries without manual specification. Dual-pathway LSTM processing with distinct dynamics for target signal enhancement and interference suppression. Context-aware adaptive fusion with multi-head attention for dynamic combination of pathway outputs. Comprehensive benchmarking against five baselines under identical training protocols and data splits. Systematic ablation, hyperparameter sensitivity (λ_s and λ_r), and noise robustness experiments across SNR levels from clean to −5 dB. Physically interpretable learned gate patterns that align with known bearing characteristic frequencies (BPFO ≈ 107 Hz, BPFI ≈ 162 Hz) without geometric priors. Reported Results FSM-Net achieves 97.74% (CWRU), 92.61% (Paderborn), and 96.54% (MIT-BIH) accuracy, consistently outperforming all frequency-unaware baselines across all three datasets. Requirements Python 3.8+, PyTorch, NumPy, SciPy, scikit-learn, matplotlib, seaborn, pandas, tqdm. How to Reproduce Place the dataset files in the paths specified in the dataset configuration section of FSM-Net.py (or modify the paths accordingly). Run python FSM-Net.py to execute the full experimental pipeline, including training all models, ablation studies, and figure generation. Results and figures will be saved to the output directory.