Frontier of Artificial Intelligence Music Generation and Digital Audio Signal Processing: From Deep Architecture Reconstruction to Multimodal Interactive Systems

Generative artificial intelligence is forcing music technology out of the studio and onto the live stage, shifting the objective from offline audio synthesis to real-time human-machine improvisation. However, deploying deep neural networks in live environments exposes a severe biological constraint: human musicians require rhythmic synchronization within a strict 50- to 100-millisecond window. Cloud-dependent, Python-orchestrated AI pipelines fundamentally fail to meet this threshold. This paper examines the rigorous architectural dismantling required to eliminate computational latency. The paper analyzes the migration of inference graphs to bare-metal C++ and WebAssembly, the replacement of legacy network standards with synchronous protocols like O2, and the deployment of causal neural audio codecs compressing speech to sub-1 kbps. Beyond hardware, the paper dissects empirical advances in live music agents, demonstrating how reinforcement learning and predictive anticipation algorithms mask residual latency by shifting AI from passive reaction to proactive foresight. Achieving zero-latency musical collaboration demands aggressive hardware-software co-design and the total abandonment of static evaluation metrics in favor of dynamic, interaction-focused assessments.