This paper introduces compact music-inspired computing. We propose a music-inspired optimization technique with minimal computational cost. The aim is to reduce the memory storage capacity required by the classical harmony search algorithm (HSA) while improving their performance. Therefore, we propose three compact harmony search algorithms. The main idea is to represent the harmonies stored in the memory by a probabilistic density function (PDF) rather than storing each one of them. Three new algorithms are introduced. The proposed algorithms include compact harmony search algorithm (cHSA), compact improved harmony search algorithm (cIHSA), and compact differential harmony search algorithm (cDHSA). The proposed algorithms are compared with their original version using benchmarks from the IEEE CEC2014. In order to evaluate the performance of our approach, the nonparametric Wilcoxon's rank sum test and Freedman test are used. The obtained results show that the proposed algorithms are very competitive. Furthermore, the computational cost is reduced. Thus, proposed algorithms are suitable for hardware implementation.