> ## Content Index > Fetch the complete content index at: https://sreekars-journal.labvion.com/llms.txt > Use this file to discover other available public pages before exploring further. # A prediction is the end of the route, not the whole project - URL: https://sreekars-journal.labvion.com/tm12-going-back-to-the-input-again/ - Published: 2025-12-28T12:30:00.000Z - Updated: 2026-10-29T08:13:31.000Z - Description: The voice project ends each processing pass with OFF, ON or noise, but the prediction is only the last visible part of the route. The export expects 16,000 raw samples at 16 kHz and uses a 650-value feature representation for three classes. The application has to supply that input before the mo - Author: Sreekar - Tags: TinyML, #Import 2026-10-29 10:08 The voice project ends each processing pass with OFF, ON or noise, but the prediction is only the last visible part of the route. The export expects 16,000 raw samples at 16 kHz and uses a 650-value feature representation for three classes. The application has to supply that input before the model can make a useful decision. The live microphone result made that connection more interesting. It was less reliable than the validation screen suggested, so the input and its processing could not remain invisible background. Timing and current measurements raised a similar question: what exactly does a number cover? Following the sound, the representation and the electrical interval together gives me a more useful account of the project than treating recognition, speed and energy as three independent results. The first question is what the microphone gave the program. The INMP441 sends digital audio, but the application still has to read the right format and interpret its values. The 16 kHz sample rate, the 32-bit storage and the shift in the conversion aren't details I can ignore once the wiring is complete. They affect the input before the model gets a chance to do anything. Then there are the recordings used for learning. One hundred one-second clips is a set I can actually examine. The 82 training and 18 test samples have separate roles, and the validation allocation is another distinction. If I don't know how those examples were collected, I don't know how far their variety reaches. A familiar word doesn't make two recordings equivalent. Feature processing connects those inputs to the classifier. The MFCC settings are no longer just a page of values between the dataset and training screens. They determine how the audio is represented. The live application needs compatible processing, and I still want to understand the implementation more deeply instead of treating the acronym as an explanation. The validation screen gives me a result and a warning about how easily I read results. The overall 88.2% was the obvious thing to notice. The noise row being mistaken for ON was the better starting point for another question. A score can be useful without telling me everything about the behaviour, especially on a different microphone or in live use. The poorer live behaviour leaves an open causal question. A mismatch between the training recordings and live input is a possible explanation, not a demonstrated cause. I want the actual recordings behind a confusing prediction, because that lets me follow a specific case instead of making increasingly broad guesses about the model. Time gives me another route through the same system. The deployment screen's 3 ms estimate belongs to a Cortex-M4F target at 80 MHz, rather than a measured result on our board. The application timer covers a particular call. The SDK separates processing and classification. Those numbers can all be genuine while describing different things. The label beside a time has become almost as important as the time itself. Energy pushes that question further. The profiler measures what the supply delivered during a selected interval. A calculated 46.3 mJ for the displayed window doesn't identify a neural-network-only contribution. I need the matching code and boundaries to say which activities were inside it. The 100 ms wait inside the marker pulse makes that especially clear. What I like is how these questions connect. An audio problem can change the prediction. A processing choice can change time and memory. A wait can change energy without changing the model at all. The program, signal and circuit keep reaching into one another. I can't fully understand one by pretending the others are settled background. There’s a practical way to keep that from becoming overwhelming: follow one example or interval at a time. Which recording? Which model version? Which feature settings? Which firmware and trace? Those details make the question smaller. They also make it possible to return to the project without relying on a general memory that something worked once. Edge Impulse handles a great deal of the training and export. The microphone, libraries and profiling tools provide other parts. My work is in learning how to bring them together, understanding the assumptions and following their behaviour on the device. That’s a worthwhile job without claiming I every part underneath it. The guidance I received belong in that account too. An explanation can help me see the right question, but I still have to check it against the actual input, configuration or code. I want to be able to explain the connection after the chat is closed. When I can't, that shows me where the next piece of learning is. The input and measurement questions belong with the project. They are more specific now than the broad feeling that the live result was disappointing. I can follow the sample representation, the processing window and the measurement boundary separately, then see how each contributes to the complete route. The saved code makes some of those connections particularly clear. Mic\_Test narrows shifted samples to int16, while inference casts the shifted result directly to float. The profiling pulse includes an extra 100 ms wait before the timer, and the loop later delays for a second. Each detail can change the interpretation of a number without changing the three class names. I can use those concrete relationships to explain the pipeline while keeping a possible cause of poorer live recognition separate from a demonstrated fix. The three labels are still there at the end of the route. What keeps pulling me back is everything I can now ask before them. I want the next prediction to come with an input I understand and a measurement I can explain. That feels like a better reason to reopen the project than simply trying to make the percentage on the screen go up.