跨计算平台音频指纹提取的功能和速度可移植性

Fu-Hai Frank Wu, J. Jang
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引用次数: 4

摘要

音频指纹识别技术(AFP)是一项对响应性、准确性和对噪声的鲁棒性要求极高的计算能力的技术。在本研究中,我们利用并行语言OpenCL来提高AFP系统中指纹提取的计算速度。特别是,我们还探索了不同平台的功能和速度可移植性。实验结果表明,该方法在不同的编译器(如Microsoft Visual Studio和Linux GCC)上使用了保守的语法,并且通过语法检查可以保证函数的可移植性。另一方面,速度可移植性要求针对特定平台对不同子模块进行优化。
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Function and speed portability of audio fingerprint extraction across computing platforms
Audio Fingerprinting (AFP) is a technology, which requests huge computing power for responsiveness, accuracy, and robustness to noise. In this study, we make efforts to improve the computing speed of fingerprint extraction in AFP system by parallelism language OpenCL. Especially, we also explore the function and speed portability across different platform. The experimental results show that the portability of function is trivial by syntax check and utilizes conservative syntax for different compilers, such as Microsoft Visual Studio and Linux GCC. On the other hand, the speed portability requests much works for optimization of different sub modules for the specified platform.
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