Acoustic Machine Learning Protocol

Abstract

The Acoustic Machine Learning Protocol (AMLP) establishes the first comprehensive, open, vendor-neutral standard for describing, composing, and executing audio processing pipelines that integrate Digital Signal Processing (DSP) and Machine Learning (ML) components.

While initially developed for AI enabled embedded devices and audio plugin development, AMLP goes further in this domain to serve as a universal protocol for any system that processes acoustic signals with machine intelligence—from consumer electronics to industrial acoustic monitoring, telecommunications to biomedical signal processing, spatial audio rendering to network audio distribution.

Key Features

• —Declarative, human-readable specification format for hybrid DSP-ML pipelines
• —Real-time-safe runtime interface with deterministic latency characteristics
• —Framework-agnostic model interchange format supporting modern quantization
• —Verifiable execution semantics for trusted AI inference
• —Standardized quality assessment frameworks based on open-source metrics
• —Native IAMF/Eclipsa Audio support for spatial audio
• —Network audio integration for professional and broadcast applications
• —Model Context Protocol integration for AI ecosystem interoperability

The Problem

Audio processing has undergone a fundamental transformation. Modern systems increasingly incorporate neural networks for tasks traditionally performed by handcrafted DSP algorithms: noise suppression, acoustic echo cancellation, beamforming, speech enhancement, sound classification, adaptive filtering, spatial audio rendering, and quality enhancement.

Yet this widespread adoption lacks any unifying standard. Each implementation uses proprietary approaches, creating redundant engineering effort, platform fragmentation, performance unpredictability, framework lock-in, deployment complexity, and security gaps.