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Machine Learning · Mini Project
Speech Emotion Recognition
Detect emotion from voice using spectrogram features
2 weeks build
Overview
Classifies a speaker's emotion from audio alone. MFCC and mel-spectrogram features feed a small CNN trained on standard emotion corpora, with live microphone capture so you can demo it on your own voice rather than a pre-recorded clip.
What makes it stand up
- 01Live microphone demo, not just a folder of test files
- 02MFCC, chroma and mel-spectrogram feature pipeline
- 03Augmentation with noise, pitch and time shifts
- 04Per-emotion confusion analysis and where it fails
- 05Training notebook you can rerun on a free GPU tier
Module breakdown
- 01
Audio
Loading, trimming and augmentation.
- 02
Features
MFCC, chroma and spectrogram extraction.
- 03
Model
CNN architecture, training and tuning.
- 04
Demo
Record or upload, with per-class confidence.
After this, you will be able to
- Explain what an MFCC is in one clear sentence
- Demo it live on your own voice
- Discuss why acted emotion datasets overstate accuracy
Technology stack
- Python
- Librosa
- TensorFlow
- NumPy
- Streamlit
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 2 weeks
- Difficulty
- Rating
- 4.5 / 5 · 211
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support