AI-Driven Monitor Mixing for Performers

Monitor engineers juggle dozens of variables: stage volume, wedge placement, performer movement, and ever-changing room acoustics. Feedback is a constant threat, especially with high-SPL wedge systems. Manual EQ notching is reactive and time-consuming, often requiring a soundcheck that eats into rehearsal time.

Performers also have subjective preferences that can shift mid-show. A guitarist might want more low-end in their wedge during one song, then less in the next. Traditional workflows rely on the engineer’s memory and quick fader moves, which can be inconsistent.

SSOUNDS integrates machine-learning algorithms into its DSP platform that continuously analyze the monitor system’s transfer function. By comparing the output of the wedge to the ambient stage sound, the AI can predict feedback frequencies before they ring out. It applies surgical, phase-coherent notches in real time, preserving the tonal balance of the mix.

This predictive approach is a leap over traditional feedback suppressors, which react after a ring has started. The AI learns the room’s modal behavior during the first few minutes of a show and adapts as the venue fills with audience or as humidity changes. The result is higher gain-before-feedback without sacrificing clarity.

During rehearsals, SSOUNDS monitor systems can record each performer’s mix adjustments—fader moves, EQ changes, and effect sends—along with time-stamped song metadata. The AI identifies patterns: for example, the lead vocalist consistently boosts reverb on ballads or the drummer asks for more click in fast tempos.

At show time, the AI suggests starting mixes based on that data, saving the engineer from building from scratch. Performers can also use a tablet app to tweak their own mix, with the AI learning their preferences over multiple shows. This creates a personalized monitor map that adapts to setlist changes.

Line checks are often the bottleneck in load-in. SSOUNDS’ AI can automate parts of the process: when a performer speaks or plays into a mic, the system identifies the input channel, checks polarity, and verifies signal path integrity. It can even detect common issues like phantom power failure or cable shorts by analyzing impedance curves.

For bands, this means a full 16-channel line check can be completed in under two minutes. The engineer reviews a dashboard of green/red indicators and only intervenes on flagged problems. This frees up time for creative mixing and artist comfort.

SSOUNDS wedges and IEM systems are built with onboard DSP that runs the AI engine locally, minimizing latency. The AI communicates with the mixing console via a lightweight protocol, sending metadata (e.g., predicted feedback frequencies, mix suggestions) without adding processing load to the console.

Engineers can override AI decisions at any time. The system is designed as a co-pilot, not an autopilot. For example, if a performer wants an unconventional EQ curve, the AI learns that preference and stops suggesting corrections. Over a tour, the AI becomes attuned to each artist’s quirks.

As AI models become more sophisticated, SSOUNDS envisions systems that can predict monitor mix needs from a setlist alone, using genre-specific training data. Imagine a system that knows a metal band needs more kick drum in the wedges than a jazz trio, and presets accordingly.

Another frontier is spatial audio for monitors: AI could automatically pan elements in a performer’s wedge mix to match their stage position, creating a more immersive and less fatiguing experience. SSOUNDS is actively researching these capabilities, always with the goal of empowering engineers, not replacing them.