Implementation of frequency analysis modules for audio coding on GPU

• Main Author: Meher, Subrat.
• Other Authors: Ian Vince McLoughlin
• Format: Final Year Project
• Language: English
• Published: 2011
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: http://hdl.handle.net/10356/44844
• Institution: Nanyang Technological University

Advanced Audio Coding (AAC) is the current MPEG standard for lossy compression of digital audio, which provides better sound quality than MP3. It involves several computation-intensive stages, e.g.,implementation of filter bank and psycho-acoustic algorithm. The discrete Fourier transform (DFT) is a commonly used tool to perform frequency analysis for psycho-acoustic model while Modified discrete cosine transform (MDCT) is used for realization of filter bank. The implementation of DFT and MDCT being computation intensive, it is important to implement them efficiently to achieve higher speed performance and lower power consumption. The tremendous evolution of GPU has given rise to a new computing paradigm called general purpose GPU computing (GPGPU) or simply GPU computing. GPGPU leverages on the enormous computing resources of GPU for data-parallel scientific and engineering applications. Under the GPU computing model, the sequential component of a given application is executed by the CPU, while the computation-intensive data-parallel component is executed concurrently by the multiple cores in GPU. In order to facilitate the use of GPU as a parallel processor to run high-level programs without using graphics oriented APIs (like GLSL, HLSL and Cg), GPGPU APIs (like CUDA, OpenCL and DirectCompute) were developed. Such APIs have been used to successfully accelerate several scientific computation and engineering applications with high computational requirement.