Most acoustic ML projects do not fail on the model. They fail on the input data. Teams spend months debugging accuracy drops that turn out to be dropped packets, shifted timestamps or a microphone that never captured the frequencies the defect lives in.

A microphone for machine learning has four requirements that classic measurement mics were never designed for:

1. Bandwidth beyond human hearing

Most speech models are trained on audio up to 16 kHz. Machines do not: bearing wear, leaks, discharge and material response carry features far above 20 kHz. The MPK801 records from 20 Hz to 80 kHz, so the model sees the whole picture. Sampling runs up to 192 kHz, with bit depth scaling by rate (31-bit at 48 kHz, 26-bit at 96 kHz, 21-bit at 192 kHz).

2. Verified data integrity

Standard audio drivers hide packet loss; the audio just gets shorter. A human never notices; a model trains on corrupted sequences. The MPK801 tracks every packet, so you know your dataset is complete or exactly where it is not.

3. Timestamps

Correlating audio with PLC events, camera frames or sensor data requires knowing when each millisecond of audio happened. MPK801 audio is delivered as timestamped 1 ms snippets.

4. Multi-channel that scales

One microphone is a demo, arrays are a product. MPK801s run on an Ethernet switch today; the ARK141 Sensor Hub adds synchronization to within 100 ns across up to 14 mics per hub.

ALSA driver for out-of-the-box OS compatibility, plus a Python API for sensor monitoring, remote configuration, and firmware updates. One of our first external users had spent two years getting conventional microphones to stream reliably into their ML stack. That is the time this microphone gives you back.

Start with a Development Kit

One microphone and a PEK481 Network Adapter are enough to get real 80 kHz timestamped audio into your pipeline and find out whether it changes your model. Convinced and want to roll out in production? Contact us directly.

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