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| title: LWM-Spectro Lab | |
| emoji: 🔍 | |
| colorFrom: purple | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: "6.0.1" | |
| app_file: app.py | |
| pinned: false | |
| short_description: 'Exploring spectrograms, LWM embedding, and its evaluations.' | |
| # LWM-Spectro Lab | |
| Interactive lab for exploring **LWM spectrogram embeddings** on Sub-6 GHz I/Q baseband data. | |
| Browse the bundled DeepMIMO-derived samples (LTE / WiFi / 5G, 128×128 spectrograms, 10.5k total), | |
| compare LWM embeddings against raw spectrogram vectors, and probe precomputed MoE embeddings for joint SNR/mobility. | |
| **What you get** | |
| - **Data slices:** LTE / WiFi / 5G Sub-6 GHz I/Q baseband spectrograms with modulation, SNR, and mobility labels; 500 samples per SNR per tech (7 SNRs). | |
| - **Visualization:** Peek at the raw 128×128 Sub-6 GHz I/Q baseband spectrograms, and compare LWM tech-specific embeddings vs. normalized raw vectors via balanced t-SNE. | |
| - **Lightweight probes:** Run k-NN prototypes for per-tech modulation recognition and joint SNR/Doppler classification using the cached MoE embeddings alongside raw baselines. | |
| ## Features | |
| - Filtered gallery of raw Sub-6 GHz spectrograms (tech/SNR/modulation/mobility). | |
| - t-SNE with SNR-balanced sampling for LWM embeddings vs. raw spectrogram vectors. | |
| - Adaptive k-NN probes for modulation (per tech) and joint SNR/Doppler (MoE vs. raw) performance. | |
| ## Usage | |
| 1. Select the **Spectrograms** and **t-SNE Analysis** tabs to explore embeddings. | |
| 2. Switch to **Modulation Classification** or **Joint SNR/Doppler Evaluation** to run the k-NN prototype with adjustable train/test splits. | |
| ## Tab Cheat Sheet | |
| - **Spectrograms:** Inspect raw 128×128 Sub-6 GHz I/Q baseband spectrograms per technology/SNR/modulation/mobility before feature extraction. | |
| - **t-SNE Analysis:** Recreate the SNR-ordered scatter plots from `plot/plot_tsne.py` with balanced sampling across SNRs. Example: hold SNR fixed and color by modulation to see how modulation clusters separate (or collapse) as SNR changes. | |
| - **Modulation Classification:** Benchmark a lightweight k-NN probe on LWM embeddings vs. raw inputs for each technology. | |
| - **Joint SNR/Doppler Evaluation:** Compare cached MoE embeddings and raw spectrograms on the 14-way SNR/mobility task. | |