This repository provides implementations of the Dynamic Time Warping Barycenter Averaging (DBA) algorithm in multiple programming languages (Java, Python, MATLAB, and Cython). The DBA algorithm computes the average of a set of time series under Dynamic Time Warping (DTW) by iteratively aligning each series to the current average and updating the average as the mean of aligned points. The repository includes the core algorithm, a multivariate extension, and versions with warping window constraints. The primary implementation is in Python (DBA.py), which is self-contained and requires only NumPy. The code is well-structured and demonstrates the iterative DTW-based averaging process, which is a non-trivial approach to time series averaging.