Spectrogram
What is Spectrogram?
A spectrogram is a visual representation of the frequency spectrum of signals over time, also called a frequency-time map. In Edge AI, spectrograms allow devices to analyze audio, vibrations, or sensor data locally, enabling fast, real-time insights without cloud dependency.
A spectrogram converts audio or sensor signals into a 2D image showing frequencies (y-axis) over time (x-axis), with intensity represented by color. This helps Edge AI models detect patterns, anomalies, or events efficiently at the source.
Why Is It Used?
Spectrograms are used to extract meaningful features from complex signals, aiding tasks such as speech recognition, predictive maintenance, and environmental monitoring—all directly on Edge devices.
How Is It Used?
Data Acquisition – Sensors capture audio, vibration, or electromagnetic signals.
Transformation – Signals are processed using Fourier or Wavelet transforms.
Model Input – Resulting spectrograms feed into AI models for classification, anomaly detection, or real-time alerts.
Types of Spectrogram
Standard Spectrogram – Basic frequency-time representation.
Logarithmic Spectrogram – Emphasizes low-frequency components.
Mel Spectrogram – Matches human hearing sensitivity, popular in audio AI.
Wavelet Spectrogram – Better for non-stationary signals in industrial Edge applications.
Benefits of Spectrogram
Real-time Analysis – Enables immediate decision-making at the Edge.
Reduced Latency – Processes signals locally, minimizing cloud dependency.
Improved Accuracy – Enhances AI models’ ability to detect anomalies and patterns.
Versatile Applications – Supports audio, IoT sensors, and industrial Edge AI solutions.