signaltagger

signaltagger is a software tool designed for tagging and annotating digital signals. It facilitates the process of labeling specific segments or events within time-series data, which is commonly found in fields such as signal processing, machine learning, and scientific research. The primary purpose of signaltagger is to enable users to systematically mark and categorize parts of a signal, making it easier to analyze, train models, or conduct further investigations.

The functionality of signaltagger typically involves a graphical user interface that allows users to load signal data, visualize it over time, and then draw or select regions of interest. These regions can be assigned specific labels or tags, providing a structured way to describe the content of the signal. For example, in audio signal processing, signaltagger might be used to label segments containing speech, music, or specific sounds. In sensor data analysis, it could be used to identify periods of anomaly, normal operation, or specific events.

The output of signaltagger is usually a dataset of annotations that can be exported in various formats, compatible with other analytical tools or machine learning frameworks. This allows for the creation of labeled datasets essential for supervised learning tasks. The tool aims to simplify the often laborious process of manual signal annotation, improving efficiency and accuracy for researchers and developers working with temporal data.