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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
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For a long time, retrieval lived quietly in the background. It powered search boxes, filters, and internal tooling—important, but rarely the center of attention. AI changed that. In modern AI-backed products, retrieval is no longer an implementation detail. It sits directly on the critical path between user intent and
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Edge AI is transforming how devices interact with the real world, from smart speakers to pet care devices. A critical challenge in deploying these models is robustness in varied acoustic environments. While collecting massive labeled datasets can help, real-world constraints often limit data availability, especially for specialised domains like pet
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Introduction Audio is one of the hardest signals to work with in real-world ML systems. Unlike images, it is continuous, noisy, and deeply tied to the environment it is captured in. At Hoomanely, we build smart pet products that live inside homes - kitchens, living rooms, and feeding areas -
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When your edge device captures thousands of audio clips in real-world conditions, the question isn't just "what's happening?" — it's "what's going wrong?" At Hoomanely, our Everbowl continuously monitors a pet to provide preventive healthcare insights for pets. But
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Machine learning promises automation at scale, but building reliable audio classification systems reveals a persistent challenge: ground truth validation. When we set out to build an edge-deployed dog sound classifier, we explored every path to automated labeling—and each one led back to the same conclusion: human perception isn'
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Introduction Building machine learning systems for the edge sounds exciting—until you realize that most real-world edge problems begin with a simple, uncomfortable truth: you don’t have labeled data. Not enough of it. Not the right kind. And certainly not data that reflects the messy environments your devices will