Back To Basics: Speakers and Power Levels

Was discussing a project with a client when they mentioned needing a “5-Watt” speaker for their product. I asked how they arrived at that number, and they said they’d used one previously and liked its loudness. I want to clarify a common mistake folks make. More Watts Doesn’t Always Mean More Volume. In fact, Watts might not even be the right way to choose a speaker. Let’s discuss this.

Speaker Sound Level

Speaker loudness is actually measured by Sound Pressure Level (SPL), expressed in decibels (dB), not watts. Watts only indicate how much electrical power a speaker can handle. SPL tells you how loud a speaker will get at a given distance and power input. Specifically, speaker sensitivity measures loudness in decibels at 1 meter away using just 1 watt of input. This is your real guide for loudness, and you will find this value in good speaker datasheets.

To get just a 3dB increase in volume, you must double the power. So, if one speaker has a sensitivity rating of 90dB and another is rated at 80dB, the first speaker is significantly louder at the same wattage. For instance, a 50-watt speaker with a 90dB rating will outperform a 100-watt speaker rated at 80 dB. Why? Because the higher sensitivity speaker converts electrical power into sound more efficiently.

SPL(dB) = Sensitivity(dB) + 10 log(Power)
  For the 90dB speaker at 50W : 90 + 10 log(50) = 90 + 16.99 ≈ 107dB
  For the 80dB speaker at 100W: 80 + 10 log(100) = 80 + 20 = 100dB

Remember, sound decreases by approximately 6dB each time the distance doubles, so you use this a tip to estimate levels at longer distance from the speaker. A speaker producing 90dB at 1m drops to about 84dB at 2m.

BTW fun fact, humans perceive a 10 dB increase as roughly twice as loud, but this actually requires about ten times more power.

So next time you’re selecting speakers, first check the sensitivity rating (dB/W/m) and not Watts alone.

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Advanced Tech: Neuton Edge AI

I got notified about Neuton AI, when Nordic Semiconductor acquired them last week. I have been playing around with the platform and the tools, and I am actually quite mind blown by what it can do.

Edge AI

Neuton is a no-code TinyML platform that builds ultra-compact neural-network models for small microcontrollers. You upload a CSV, press train & it grows the network, and you export it as a plain-C library that can be put on any microcontroller platform. The ridiculous part is the size of the generated model file is only around 5KB which is usually 10x lower than other ML platforms like TFlite, AutoKeras maintaining same or better accuracy. Small size also means inference time can be around 2ms. Faster inference implies lower battery usage too.

Edge AI

I really wanted to learn how they achieve this extreme size reduction. All the videos and content on the website are slightly vague. Their core tech is proprietary. Seems to have 2 US patents that cover architecture-free self-organisation and a parallel global search for weights and feature selection. What it means in practice is that the model stops growing the moment accuracy stalls, so there is nothing left to trim. They clearly mention they don’t use common training methods like back propagation or stochastic gradient descent. Each new neuron connects only to the most critical inputs or features rather than every possible input. This keeps the weight count low.

The platform keeps validation curves and lets you pick and download any smaller checkpoint if you prefer size to accuracy. It also has a signal processing engine to help with preprocessing of the data. Because the network connects only to the most informative inputs, it plays well with time series from IMUs, vibration sensors, ECG, and radar. I think this will let product teams skip a lot of manual DSPs and focus on features. Platform is also free to use, not sure if Nordic Semi fronting all the costs for the training runs.

For me personally, Neuton feels like a strong player for my future ML projects. Try it out yourself and see if it’s worth it for you.

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