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We describe the implementation of monaural audio source separation algorithms in our toolkit openBliSSART (Blind Source Separation for Audio Recognition Tasks). To our knowledge, it provides the first freely available C+驴+ implementation of Non-Negative Matrix Factorization (NMF) supporting the Compute Unified Device Architecture (CUDA) for fast parallel processing on graphics processing units (GPUs). Besides integrating parallel processing, openBliSSART introduces several numerical optimizations of commonly used monaural source separation algorithms that reduce both computation time and memory usage. By illustrating a variety of use-cases from audio effects in music processing to speech enhancement and feature extraction, we demonstrate the wide applicability of our application framework for a multiplicity of research and end-user applications. We evaluate the toolkit by benchmark results of the NMF algorithms and discuss the influence of their parameterization on source separation quality and real-time factor. In the result, the GPU parallelization in openBliSSART introduces double-digit speedups with respect to conventional CPU computation, enabling real-time processing on a desktop PC even for high matrix dimensions.