tdetection

Tdetection, short for temporal detection, refers to the task of identifying the occurrence of events or patterns within time-based data streams. It is used across domains such as audio, video, sensor data, and multi-modal streams, and is closely related to event detection and onset detection in time-series analysis. The goal is to signal when something meaningful happens, often with precise timing information.

Techniques in tdetection combine signal processing and machine learning. Common feature representations include spectrograms and MFCCs for audio, optical flow or frame differences for video, and statistical features for sensor data. Models range from traditional classifiers and matched filters to modern deep learning approaches such as convolutional neural networks, recurrent networks, and transformer-based architectures. Systems can be trained in supervised, semi-supervised, or unsupervised settings, and may operate in real time or on offline data.

Data types and evaluation play a central role. Datasets typically provide timestamps or segment labels for events. Evaluation metrics frequently used are precision, recall, F1 score, and, for segmentation tasks, intersection-over-union or event-level average precision. Robust tdetection often requires handling noise, overlapping events, variable timing, and domain shifts.

Applications span security and surveillance, activity and gesture recognition, music information retrieval (such as note onset or beat detection), environmental and industrial monitoring, and medical signal analysis. Challenges include labeling difficulty, real-time constraints, and generalization across domains. Ongoing research blends signal processing with advances in deep learning to improve accuracy, efficiency, and interpretability in tdetection systems. See also onset detection, event detection, anomaly detection, and time-series analysis.