Dynamic Generation of Brand Sound DNA for Intelligent Electric Vehicles: An Active Sound Design System Based on Deep Learning

Abstract

(ASD) widespread adoption of intelligent electric vehicles (IEVs) has eliminated traditional engine noise, raising pedestrian safety concerns and accelerating product homogeneity, which severely weakens brand auditory identity. Active sound design (ASD) has thus become a core technology for reshaping brand sound DNA and enhancing in‑cabin immersion and interaction. However, existing ASD systems largely rely on static concatenation of audio samples or fixed rule‑based parameter mapping, struggling to cope with complex driving scenarios and failing to deliver dynamic evolution or personalized expression of brand sound DNA. To address this limitation, this paper presents a deep learning‑based system for dynamic brand sound DNA generation and active sound design. We construct a multidimensional acoustic feature corpus of brand sound DNA and quantitatively decode the deep mapping between acoustic parameters and brand emotional semantics (e.g., sense of technology, sportiness, and luxury). A sequential audio generation model is designed by fusing a conditional generative adversarial network (cGAN) with a long short‑term memory (LSTM) network. An online adaptation mechanism built on deep reinforcement learning is introduced, which collects real‑time physiological feedback and subjective evaluations to dynamically fine‑tune the sound generation policy, enabling personalized evolution of the brand sound. This work provides an innovative technical pathway for auditory interaction design in IEVs and advances automotive acoustic engineering from static presets toward dynamic intelligent generation.