November 2026

The Audio DSP Universe Expanded
Today's leading platforms are evolving beyond fixed audio DSP into fully adaptive computational audio systems.
Editorial By J. Martins

  For a magazine with roots in do-it-yourself audio and inspiration from self-made entrepreneurs and inventors, looking at how modern audio product development teams actually work can change our perspective on products—and on what constitutes good design. And now that perspective is changing again. No matter how tightly engineering and design teams are integrated, audio product development involves multiple disciplines, each with its own priorities and its own definition of "high quality." Those definitions work well enough within each domain—until the pieces are brought together. Then every design decision has to be considered in terms of its effect on the complete system.

Most audio products today are developed by cross-functional program teams, not by a single "audio engineering" group. A complex system such as a modern soundbar requires a systems lead or technical program manager to coordinate acoustics, electronics, RF, embedded software, mechanical engineering, industrial design, test, quality, sourcing, and manufacturing. And none of those teams defines the relevant "quality" parameters on their own. That becomes particularly obvious when those parameters involve subjective criteria—what we often conveniently describe as "tuning" the final sound.

When we review, compare, measure, and judge finished products by their "qualities," it is easy to forget that products might have been designed around cost, reliability, reuse of existing components and processes, component availability, supply-chain risk, and production schedules. Performance is inevitably shaped by all of those.


Larger organizations add lead roles responsible for different priorities, including quality and technical standards. This is also why larger companies appoint system architects with experience across several domains. Even system-level hardware roles now require engineers to work across electrical, software, mechanical, RF, and manufacturing disciplines. And yet, it would still be an oversimplification to say that the system architect or audio-systems lead is responsible for final "sound quality."


The old-school audio-engineering playbook told us that sound-experience or tuning engineers translated objective capabilities into the intended product voicing through listening tests, tuning tools, and content-based evaluation. But is that still the case?


Where then should subjective assessments of "sound quality"—or the end user's perception of performance and value—enter the product-development workflow? The obvious answer is that subjective assessment should be a continuous development workstream, not a final listening session held after engineering is supposedly "done." It should accompany development and continue after launch through field feedback. DSP can be the principal differentiator in a modern soundbar, but only when its tuning is grounded in acoustics, controlled listening tests, and robust system validation.

Until recently, audio-system and DSP engineers concentrated primarily on the signal path: crossover and limiter strategies, EQ and dynamics, bass management, calibration, protection algorithms, codecs, and so forth. Inevitably, every team could regard its contribution as decisive to the final performance and DSP would either destroy all the careful work that came before it or, preferably, perform miracles on problems nobody else had managed to solve.

One way or another, signal processing has always been decisive in determining how an audio system performs. Now, DSP is even more significant, because AI is fundamentally changing audio product architectures and expanding what can be done with the audio signal itself.

As we worked on another issue of audioXpress focused on DSP—or, more broadly, audio signal processing—the landscape shifted again. Major chipset and processor vendors are introducing new on-device AI platforms, while increasingly integrated architectures can move much larger quantities of sensor and audio data through shared memory to neural processing units (NPUs). Many forms of low latency audio processing are becoming practical.

The latest platforms are able to evolve from mostly fixed audio DSP to a fully adaptive computational-audio system. Content-aware dialogue enhancement, source separation, automated room and placement calibration, personalization, and adaptive signal processing all become viable AI-enabled audio systems expand what DSP can be asked to accomplish. That also changes how performance is evaluated. Benchmark scores or subjective performance metrics become the result of the actual listening experience, meeting user preferences, and content specific.

What is now needed is another expansion of the engineering team. AI and machine-learning specialists need to work closely with existing audio-DSP, embedded-software, acoustics, RF/platform, product, quality, and support teams. And inference latency, processor and memory utilization, power consumption, and model accuracy join the already long list of engineering metrics that need to be considered during product development.

Apparently, defining "good sound" wasn't complicated enough already.

J. Martins
Editor-in-Chief

Subscribe To audioXpress Magazine
Click here to subscribe to audioXpress magazine.