This talk addresses the limitations of content-agnostic post-processing pipelines on consumer electronics (CE) devices, focusing on how blind audio processing degrades user experience and violates artistic intent. Streaming platforms deliver a vast range of content formats over stereo channels to conserve bandwidth, requiring edge devices like TVs, soundbars, and receivers to upmix signals for spatial output. However, applying identical upmixing DSP chains to inherently stereo music as to downmixed multichannel movie audio introduces noticeable audible artifacts. The primary objective of this research is to present a low-latency, low-memory edge intelligence solution that enables real-time, content-aware multimedia classification directly on edge hardware, guiding appropriate post-processing decisions without relying on heavy computational models or upstream server changes.
The presenter evaluates existing deep learning classification methods, demonstrating that standard frame-level audio-visual models are unviable for edge deployment due to severe latency, memory footprint, and power constraints. Furthermore, compression methods such as pruning and quantization frequently sacrifice classification reliability in real-time environments, while server-side metadata embedding would require altering MPEG standards and remain incompatible with legacy decoders. To solve this, the core architecture utilizes lightweight deep learning on linguistic metadata within the MPEG standard—specifically video titles—rather than processing raw audio or visual frames. Extensions integrating neural machine translation allow for multilingual support, seamlessly feeding content classification decisions into the DSP signal processing chain across global streaming ecosystems.
Co-designing edge intelligence alongside traditional signal processing offers a scalable, robust framework for preserving artistic integrity across consumer devices. The approach avoids expensive frame-by-frame inference while remaining compatible with existing standards and legacy systems. Practical video demonstrations on commercial streaming services confirm that title-based metadata classification delivers high accuracy and reliable real-time post-processing adjustments at a fraction of the computational footprint.
