This paper presents a technique for noise and vibration reduction in low-speed sensorless control algorithm of surface mounted permanent magnet synchronous machines (SM-PMSM). The signal processing involved in high-frequency-based sensorless control algorithms has a significant effect on the vibration and noise accompanying such algorithms. Therefore, a wavelet filtering approach is proposed. This wavelet filter is capable of extracting the position information even at low values of injection voltage amplitude where conventional filters fail to extract the information. This leads to a reduction in the machine's noise and vibration. Furthermore, an optimization using hardware-in-the-loop method based on genetic algorithms was implemented in real time to obtain the optimum value of the injected voltages amplitude and frequency causing the least possible vibration with acceptable position estimation accuracy. Consequently, a significant reduction in vibration and noise was achieved. The proposed techniques were implemented on a 2-hp SM-PMSM motor both experimentally and through simulations. Comparisons between numerical and test results confirm the validity of the proposed techniques.