Emous V1 !!better!! Review

| Metric | Value | |--------|-------| | Accuracy (on RAF-DB + IEMOCAP) | 84.3% | | Latency (full pipeline) | 45ms (M1 CPU) / 22ms (NVIDIA T4) | | Model Size | 14.8 MB | | RAM Usage | ~180 MB | | Power Draw | 1.2W (inference on Raspberry Pi 4) |

: Because it runs entirely via browser-based emulation, it bypasses the need for complex hardware setups or local emulator installations. Technical and Educational Value EmuProjects - Emupedia emous v1

Input Streams │ ├─► Video Module (FaceNet-based) ├─► Audio Module (wav2vec2 fine-tuned) └─► Text Module (DistilBERT) │ ▼ Fusion Layer (Attention-based) │ ▼ Emotion Predictor (Softmax over 7 classes) │ ▼ [JSON Output] "emotion": "joy", "confidence": 0.87 | Metric | Value | |--------|-------| | Accuracy

“Bridging human emotion and machine response.” emous v1

Because emous does not utilize deep learning transformers, it processes text almost instantaneously. It uses a hybrid approach of lexical hashing and weighted decision trees, ensuring that sentiment analysis does not become a bottleneck in real-time applications.