Learn Designing the Best Audio Subsystems for Embedded Devices

26 Jun Learn Designing the Best Audio Subsystems for Embedded Devices

Analog vs Digital MEMS Microphones? Make the Right Choice for Your Embedded Audio Design

Designing an audio subsystem for embedded devices means processing the entire signal chain responsible for capturing, processing, and reproducing sound. The typical components for an audio subsystem include microphones, codecs, meaning ADCs/DACs, amplifiers, and speakers. We do have firmware logic as well that manages their operation. While audio engineering is a deep field of its own, in the context of embedded systems, it focuses on making audio functional, efficient, and reliable within hardware and software constraints.

1. Introduction to the Audio Subsystem

For many electrical engineering students, audio design is often touched upon lightly. Usually, we study it within Digital Signal Processing courses or electives that cover topics like Fourier transforms, FIR/IIR filters, and signal quantization. It’s rarely explored hands-on, system-level, unless we take up audio electronics or multimedia hardware labs. This creates a gap between theory and application, especially in areas like microphone interfacing, I²S protocol implementation, or real-time voice recognition on microcontrollers. In industry, however, the audio subsystem is one of the most practically relevant parts of many modern consumer and industrial devices. These devices are everywhere, from smart earbuds and voice assistants to intercoms and hearing aids.

In smart wearables like earphones, the importance of a well-designed audio subsystem is not overstated. Audio is how users interact. They speak commands, hear back feedback, and feel functionality through sounds. Poor audio performance leads to frustration, miscommunication, and product dissatisfaction. On the other hand, responsive, clear, and natural-sounding audio adds to usability and builds trust with technology. This makes the audio subsystem not just a technical module but also a core user experience element.

Why is audio processing important in smart wearables?

Now, one might wonder — is audio design only about capturing and playing back sound? And are real-time processing, power efficiency, and miniaturization the only concerns we face? The answer is — these are foundational, but they are not enough. For example, the audio path in a wearable must also be resilient to environmental noise and adapt to human speech characteristics. It should also be seamlessly connected with wireless protocols like Bluetooth or BLE Audio.

Additionally, security, latency, regulatory compliance, e.g., SAR, EMC, and even heat dissipation, become relevant at the system level. Therefore, designing an audio subsystem is not an isolated electrical task. It is a multi-disciplinary engineering challenge. And in it, we combine signal processing, embedded firmware, acoustics, power electronics, and human factors.

Let’s understand how to build an earpiece

Let’s anchor all this theory with a simple example. Imagine you’re building a smart earpiece that should respond to the phrase, “Hey Earpiece.” As soon as the user says it, the device must:

• Capture that voice with a microphone
• Convert the signal into a digital stream through an ADC or audio codec
• Process the data to detect the phrase in real time using a lightweight ML model
• Suppress background noise to avoid false triggers
• Save power by keeping the MCU asleep until voice is detected
• Once the keyword is identified, trigger an action, like playing a tone, connecting to a phone, or responding with voice

All of that happens within milliseconds, and often, in a device smaller than a coin. This is what makes the audio subsystem such a fascinating and critical component of embedded system design. It is not just about sound — it is about enabling meaningful, real-time interactions in resource-constrained environments.

2. Microphone Subsystem

2.1 What is a MEMS Microphone?

The microphone subsystem is where everything starts in an earpiece. If your device can’t capture the user’s voice accurately, everything that follows in the system breaks down. That’s why microphones are not just simple hardware parts. They play a critical role in making devices to listen, understand, and respond effectively.

Now, when we talk about microphones in modern embedded systems. Like compact devices like earbuds or hearing aids, we’re almost always referring to MEMS microphones. MEMS stands for Micro-Electro-Mechanical Systems. This name tells us a lot about what’s going on inside.

So, how does a MEMS mic work?

A MEMS microphone has a tiny diaphragm that vibrates in response to incoming sound waves. This is the same basic principle as traditional microphones. But instead of using coils or moving magnets like we did in older electret condenser mics, MEMS microphones use capacitive sensing. The diaphragm forms one plate of a capacitor. As the diaphragm moves due to sound pressure, the distance between the plates changes. This change is then converted into an electrical signal by the built-in circuitry.

What makes this movement to an electrical signal relevant to us? As embedded engineers building smart, battery-powered devices, its that MEMS microphones are designed to be incredibly small, low-power, and highly integrable. They come in chip-sized surface-mount packages, often just 3–4 mm wide, and many have integrated signal conditioning and even analog-to-digital conversion inside.

This is a big deal. It means we can capture voice with high sensitivity, in a package small enough to fit into an earbud shell, without needing to build complex analog circuitry around it. These microphones are also designed with environmental robustness in mind — things like moisture resistance, vibration tolerance, and dust protection — all of which are key for a wearable that’s exposed to the real world.

Why use a microphone?

In a smart earpiece, the microphone plays two important roles:

1. Capture the user’s voice clearly, even when they speak softly or in noisy surroundings.
2. Detect ambient sound, which can be used to cancel background noise, improve speech quality, or monitor environmental context.

It’s the very first step in enabling things like:

• Wake word detection (“Hey device”)
• Noise suppression
• Voice commands and phone call input
• Real-time audio feedback for ML tasks like stress detection

Without a reliable microphone, all of these features fall apart.

2.2 Analog vs. Digital MEMS Microphones

Once you’ve settled on using a MEMS mic, you’ll run into your first real design decision: Should you use an analog or a digital microphone?

Both types are MEMS-based, both are small and power-efficient, but they differ in how they deliver their output, and that has downstream consequences for your entire signal chain.

Analog MEMS Microphones

Analog MEMS microphones give a voltage signal that corresponds to the detected sound pressure. This signal is continuous like a sine wave. We need to digitize it with an external Analog-to-Digital Converter before your MCU or DSP can work with it. Often, this ADC comes as part of an audio codec IC. But in simpler systems, we can use the MCU’s built-in ADC if the performance is good enough.

At first glance, analog mics seem simpler — after all, they’re just voltage-output devices. But there’s a catch: analog signals are sensitive to noise. Any electromagnetic interference (EMI), poor PCB layout, or long trace runs can distort the signal before it reaches the ADC. That distortion can degrade your voice recognition, introduce hum or hiss, or even result in dropped audio data.

In addition, analog mics need clean, well-filtered power, and they often require external biasing and pre-amplification — all of which adds complexity to your design.

But they also have some advantages, especially when we need fine control over signal conditioning, or when working with legacy analog processing paths.

Digital MEMS Microphones

Digital MEMS microphones perform the ADC conversion internally. This means the output of the mic is no longer a voltage waveform, but a digital signal. These mics typically use formats like:

• PDM (Pulse-Density Modulation) — a high-frequency, one-bit stream that encodes audio based on the density of ones and zeros
• I²S (Inter-IC Sound) — a serial protocol specifically designed to stream audio data

From a system integration point of view, digital mics are a game-changer.

Why? Because they reduce noise susceptibility, simplify layout, and can be connected directly to the MCU using standard digital audio interfaces, without the need for an external codec or ADC in some designs.

For a smart earpiece, this is huge. It allows us to:

• Save board space by removing analog circuitry
• Use cleaner, more noise-tolerant signal paths
• Optimize for power by selectively clocking the I²S peripheral

But there are trade-offs, too. We need an MCU that supports PDM or I²S input, which not all low-cost microcontrollers offer. And while PDM mics are flexible, they do require decimation filtering in software or hardware to reconstruct the audio waveform, which consumes some processing power.

Design Considerations – What’s Best for a Smart Earpiece?

Feature	Analog MEMS Microphone	Digital MEMS Microphone
Output Type	Analog voltage	Digital PDM or I²S
Requires External ADC	Yes	No (built-in ADC)
Noise Susceptibility	High (sensitive to layout/EMI)	Low (digital signals are more robust)
Integration Complexity	Higher	Lower
Power Consumption	Generally lower	Slightly higher (due to on-chip ADC)
Firmware Interface	Simple (analog read via ADC)	Requires I²S or PDM handling
Use in Smart Earpiece	Possible, but layout-sensitive	Ideal for space-saving, noise-tolerant use

Conclusion

In general, for smart earpiece applications, digital MEMS microphones are the more common choice, especially when voice clarity, environmental robustness, and compact PCB layout are top priorities. They work well with modern microcontrollers that offer I²S peripherals and DMA streaming, which allows audio to be processed or streamed without burdening the CPU heavily.

That said, if your design needs analog processing (e.g., active noise cancellation with analog front ends) or you’re reusing a system with an existing analog audio codec, then analog mics still hold value.

The key takeaway? Choose the microphone type based on the entire audio chain — from sensor to processor to output — and not just based on the mic’s datasheet. Look at your MCU’s peripheral support, PCB real estate, power budget, EMI environment, and the specific features your product needs to deliver.