Predictive Analytics Model

Building a deep learning model that predicts speech from facial EMG signals — specifically by converting multi-channel facial EMG (electromyography) input into audio spectrograms (amplitude + phase), which can then be reconstructed into audible speech.

1. Data Input:
You use multi-channel EMG (e.g., 16 channels) recorded at 500 Hz.
Paired with that, you have corresponding audio recordings at 11025 Hz.

2. Data Preprocessing:
You slice the EMG and audio into windows (e.g., 500 EMG samples per window).
The audio is transformed into a spectrogram using STFT (Short-Time Fourier Transform).
The spectrogram is split into:
Amplitude (magnitude of the signal at each time-frequency bin)
Phase (direction of the signal — needed to reconstruct sound)

3. Model:
You built a convolutional neural network (CNN) to map the EMG input to the spectrogram.
There are two models:
One predicts amplitude
One predicts phase

Results Presentation:
1. Visual Comparison of Spectrograms (Before & After):
Show side-by-side plots of:
Ground Truth Spectrogram (Amplitude + Phase)
Predicted Spectrogram (Amplitude + Phase)

2. Waveform Audio Comparison (Reconstruction):
Let listeners hear:
The original audio (from microphone)
The reconstructed audio (from EMG prediction)