Modern consumer products are full of interconnected electrical and electronic modules to fulfill direct and indirect needs. In an automated assembly line still, most of these interconnections are required to be done manually due to the large variety of connector types, connector positions, and the soft, flexible nature of their structures. The manual connection points are the source of partial or completely loose connections. Sometimes connections are missed due to the application of unequal mating forces and natural human fatigue. Subsequently, these defects can lead to unexpected downtime and expensive rework. For successful connection detection, past approaches such as vision verification, Augmented Reality, or circuit parameter-based measurements have shown limited ability to detect the correct connection state. Though most connections emit a specific noise for successful mating, the acoustic-based verification system for electrical connection confirmation has not been extensively researched. The main discouraging reason for such research is the typically low signal-to-noise ratio (SNR) between the sound of a pair of electrical connector mating and the diverse soundscape of the plant. In this study, the authors investigated increasing the SNR between the electrical connector mating sound and the plant soundscape to improve connection success detection by employing a physical system for background noise mitigation and the successful met noise signature amplification algorithm. The solution is over 75% effective at detecting and classifying connection state. The solution has been constructed without any modification to the existing manual interconnection process.