Loudspeaker Technology and AI Models: Applications, Innovations, and Future Prospects

Abstract

This paper explores integrating AI technology with speaker systems, which has led to significant advancements in sound enhancement and personalized audio generation. As speaker technology evolved from its electroacoustic origins in the 19th century to its current digital form, AI has introduced new capabilities, enhancing sound quality and user experience. The research investigates the application of AI models, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in improving audio processing. It discusses the methodologies for training these models using large datasets and outlines the evaluation process, including audio quality assessment, response speed testing, and user experience feedback. The study concludes that while AI models significantly improve audio performance, challenges such as increased computational demands and potential latency must be addressed. Hybrid approaches combining traditional algorithms with AI models are proposed to balance audio quality with system efficiency, and future research directions include enhancing AI model stability and privacy protection.