We present a case study of optimizing a Pythonbased music recognition application on an selected Intel high performance processor. With support from Numpy and Scipy, Python addresses the requirements of music recognition problem on math library utilization and special structures on data access. However, a general optimized Python application cannot fully utilize the latest high performance multi-core processors. In this study, we survey an existing music recognition application, written in Python, to discover the effect of applying changes to the Scipy and Numpy libraries to achieve full processor resource occupancy and reduce code latency. Instead of comparing across many different architectures, we focus on Intel high performance processors that have multicores and vector registers, and we attempt to preserve both userfriendliness and code scalability so that the revised library functions can be ported to other platforms and require no extra code changes.