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Fast parallel algorithm for audio content retrieval on GPUs

机译:在GPU上检索音频内容的快速并行算法

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The search techniques audio content MIR (music information retrieval) face two major challenges: the robustness of the algorithm and the speed of this operation. In this article proposes a model of fast algorithm for the extraction of audio data by the fingerprinting technique, which is implemented on a CPU-based platform and then parallelized to run on a graphics hardware (GPU). Tests on GPU determined a success rate close to 100% and response times approximately 2 times lower than those obtained with a PC workstation, allowing searches of up to 65 commercial in real-time.
机译:搜索技术音频内容MIR(音乐信息检索)面临两个主要挑战:算法的鲁棒性和此操作的速度。本文提出了一种通过指纹技术提取音频数据的快速算法模型,该模型在基于CPU的平台上实现,然后并行化以在图形硬件(GPU)上运行。在GPU上进行的测试确定成功率接近100%,响应时间比在PC工作站上获得的响应时间低约2倍,从而可以实时搜索多达65个商业广告。

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