Optimization and Parallelization of Monaural Source Separation Algorithms in the openBliSSART Toolkit
Optimization and Parallelization of Monaural Source Separation Algorithms in the openBliSSART Toolkit
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DOI：
10.1007/s11265-012-0673-7
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发表时间：
2012-04
期刊：
影响因子：
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通讯作者：
F. Weninger;Björn Schuller
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文献类型：
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作者：
F. Weninger;Björn Schuller
我们描述了单声道音频源分离算法在我们的工具包openBlissART（盲源分离音频识别任务）的实现。据我们所知，它提供了非负矩阵分解（NMF）的第一个免费C++实现，支持统一计算设备架构（CUDA），用于图形处理单元（GPU）上的快速并行处理。除了集成并行处理，openBlissart还引入了几种常用单声道源分离算法的数值优化，可减少计算时间和内存使用。通过说明从音乐处理中的音频效果到语音增强和特征提取的各种用例，我们展示了我们的应用程序框架对多种研究和最终用户应用程序的广泛适用性。我们通过NMF算法的基准测试结果对工具包进行了评估，并讨论了它们的参数化对源分离质量和实时性的影响。因此，openBlissart中的GPU并行化相对于传统的CPU计算引入了两位数的加速，即使对于高矩阵维度，也可以在台式PC上进行实时处理。
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+ + 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.