Feature Vector Construction

Overview

Individual audio features produce sequences of values across multiple frames.

Machine learning classifiers require a consistent input representation.

VoxShield converts these sequences into fixed-length statistical summaries.

Example

Suppose an MFCC extraction produces:

40 coefficients × 100 frames

The model calculates:

Mean of each coefficient       → 40 values
Standard deviation of each     → 40 values

Total:

40 + 40 = 80 features

Proposed Feature Summary

Feature Group	Dimensions
MFCC Mean + Standard Deviation	80
Mel Spectrogram Mean + Standard Deviation	80
Spectral Features	13
Pitch	2
Zero Crossing Rate	2
Chroma	12
RMS Energy	2
Total	191

Important Implementation Requirement

The following must remain identical:

1. Feature extraction parameters
2. Feature order
3. Statistical aggregation
4. Audio preprocessing assumptions
5. Output vector dimensions

If the model is trained using one feature arrangement and inference uses another, predictions may fail or become unreliable.