Examples

Welcome to the DeepPeak examples gallery! This directory contains comprehensive examples demonstrating the key capabilities of the DeepPeak library for signal processing and peak detection using deep learning.

DeepPeak is a Python library designed for detecting and analyzing peaks in 1D signals using machine learning approaches. These examples showcase the library’s main features:

• Signal Generation: Create synthetic datasets with controllable noise and peak characteristics

• Machine Learning Classifiers: Train neural networks to detect regions of interest

• Peak Detection Algorithms: Apply traditional and ML-enhanced peak detection methods

• Visualization Tools: Plot and analyze results with built-in visualization utilities

Non-Maximum Suppression for Gaussian Pulse Detection

Non-Maximum Suppression for Gaussian Pulse Detection

DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals

DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals

DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals

DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals

DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals

DenseNet Classifier: Detecting Regions of Interest in Synthetic Signals

Generating and Visualizing Signal Data

Generating and Visualizing Signal Data

Non-Maximum Suppression for Gaussian Pulse Detection

Non-Maximum Suppression for Gaussian Pulse Detection