Design and Optimization of a Real-Time Audio Simulation Engine on Mobile Devices

Abstract

With the rapid expansion of interactive and multimedia applications on smartphones, real-time audio simulation has become one of the core components in user experience design. However, the inherent limitations of mobile platforms in terms of computational capacity, energy consumption, and strict real-time constraints have turned the design of stable and low-latency audio engines into a major technical challenge. The objective of the present study is to design and optimize an efficient architecture for a real-time audio simulation engine on mobile devices that can establish an appropriate balance between audio quality, real-time responsiveness, and resource consumption. This study was conducted using a design-oriented and experimental approach. First, a system-centered architecture based on the separation of real-time and non-real-time domains was developed. Subsequently, a set of optimization algorithms and techniques—including adaptive buffer management, voice capping and voice stealing policies, quality scaling, and conditional processing—were implemented. The proposed engine was developed on the Android platform using low-level audio APIs and evaluated through an interactive case study. The system’s performance was compared with that of a baseline implementation. The experimental results demonstrated that the proposed architecture significantly reduced latency and jitter while maintaining the real-time stability of the engine under high-load conditions. In addition, CPU usage and energy consumption were reduced in a controlled manner, and the degradation of audio quality was applied gradually and in a manner perceptually acceptable to users. Perceptual findings further indicated that users perceived controlled quality degradation as considerably more tolerable than audio instability or dropouts. The findings suggest that the design of real-time audio simulation engines on mobile platforms should be grounded in an architectural and adaptive approach. Emphasizing real-time pipeline management and intelligent control policies plays a more decisive role in achieving stable and efficient performance than increasing the complexity of digital signal processing (DSP) algorithms.