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How MelCNN learned “ghost sounds” from mislabeled silence Introduction Hallucination is usually discussed in the context of large language models—confident answers to questions that were never asked, or facts that never existed. But hallucination isn’t exclusive to text models. In audio systems, especially CNN‑based classifiers like MelCNN,
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Using large models to sanity-check edge detections Introduction Audio datasets are fragile. Unlike images or text, you cannot visually skim through thousands of audio samples and immediately know whether they belong to the right class. A barking dog, a metal clank, or background human speech can sound deceptively similar in
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The Calibration Problem Nobody Talks About When you ship a hardware product with sensors, you inherit a problem that pure software companies never face: the real world is messy. At Hoomanely, our smart bowl—Everbowl—uses load cells to measure food and water consumption and thermal sensors to track a
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Problem Modern pet‑health systems increasingly rely on thermal cameras to extract physiological signals such as eye temperature. The challenge is that thermal sensors are low‑resolution and almost never share the same optical center, field of view, or distortion model as the RGB camera doing the visual perception. In
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The Uncomfortable Truth About ML Projects Every machine learning project has a dirty secret: the model is usually not the problem. When a model underperforms, the instinct is to reach for a bigger architecture, more layers, or fancier training tricks. But in our experience building machine learning systems at Hoomanely,
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The Unspoken Tension of In-Home AI When we talk about smart home devices, the conversation inevitably drifts toward a single, uncomfortable word: privacy. At Hoomanely, we build technology that lives in the spaces of a pet parent's home. Our flagship product, Everbowl, is a smart feeding system that
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Introduction Transfer learning usually feels like a shortcut that just works. Pretrain on a large dataset, fine-tune on your task, and let scale do the heavy lifting. That approach worked well for us in vision - but audio broke that assumption in a very specific, painful way. At Hoomanely, we
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Audio classification on edge devices presents a unique engineering challenge that doesn't get enough attention in the ML community. While everyone's building massive transformer models that need GPU clusters, we needed something that could run on a Raspberry Pi and accurately detect different types of dog
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The "Cold Start" Problem in Audio AI In the lifecycle of any machine learning project, the most painful phase is often the very first one: Data Curation. Before you can train a model to detect a dog bark, you need thousands of labeled examples of dog barks. But
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AI systems are no longer passive observers. What began as chatbots, summarizers, and recommendation engines has evolved into systems that actively influence application behavior and persistent state. AI now classifies events, prioritizes alerts, triggers workflows, escalates anomalies, and shapes timelines that users and downstream systems rely on. This shift fundamentally