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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesApple’s built-in Sound Analysis classifier may not include a cat_hiss label. The exact label inventory reported for its .version1 classifier comes from a secondary article, not an Apple-published list, so check the classifier your app actually uses. If the label is missing, changing an audio threshold cannot add it; Apple’s documented alternative is a custom Core ML sound classifier.
Does Apple’s built-in classifier have a cat_hiss label?
A September 2026 DEV Community article reports that the built-in .version1 classifier includes cat_meow and cat_purr, but not cat_hiss. Apple’s API documentation explains how to inspect a classifier’s labels but does not enumerate those specific cat labels. Treat the reported inventory as a reason to check your target runtime, not as an official guarantee.
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Apple says Sound Analysis requests can identify over 300 sounds, but that broad figure does not establish coverage for every kind of cat vocalization. The framework also supports custom Core ML models. Apple’s Sound Analysis overview describes the framework and custom-model route.
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How to check the labels on the classifier you ship
Use SNClassifySoundRequest with the classifier identifier your app intends to use, then inspect knownClassifications. Apple defines that property as a string array containing every prediction label in the request’s underlying sound-classifier model. This checks the actual model at runtime instead of relying on a copied list.
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Create an
SNClassifySoundRequestfor the classifier you plan to use. -
Read and log its
knownClassificationsproperty on the OS and classifier version you intend to support. -
Check whether the returned labels include the exact class your feature needs, such as
cat_hiss. If the label is absent, the built-in classifier does not expose that class as a prediction label in that runtime.Outdated Drivers Are Slowing You Down
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See Apple’s SNClassifySoundRequest documentation for the request and label-inspection API. Confirm labels on each relevant target runtime: the documentation retrieved here does not promise a fixed cat-specific inventory.
What a missing label means—and what it does not
A classifier’s available labels are distinct from its predictions for a particular audio clip. If cat_hiss is not in knownClassifications, adjusting microphone gain or an energy threshold may affect the audio signal or a separate detection rule, but it cannot add that missing class to the model. A custom model or a separate signal-processing approach would be needed to pursue that class.
Conversely, a label being present does not guarantee that a hiss will be recognized reliably. The model’s output is a classification of sound, not an explanation of why the cat made it. A sound label alone cannot establish fear, aggression, pain, health, or intent.
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What is known about hiss predictions
The DEV Community article reports informal recordings that sometimes produced no classification, a generic cat label, or snake_hiss. It does not provide a reproducible dataset or test protocol, so these examples are not evidence of general accuracy or failure rates for cat-hiss audio.
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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →For a product decision, validate the exact model with representative hiss recordings, varied distances and backgrounds, and the conditions in which users will record. Measure outcomes against labeled examples rather than treating an anecdotal prediction as a performance guarantee.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When to use a custom sound model
If the required label is missing, Apple documents supplying a custom Core ML model for sound classification; its framework overview also points to Create ML for training custom sound classifiers. A custom model lets you define a label set that includes the class you need, but the documentation cited here does not establish its accuracy for cat hissing. That depends on the data, training, and validation for your use case.
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Choose an approach based on the actual requirement:
-
Built-in label coverage: inspect the selected classifier on the target OS.
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Custom data and labels: consider a custom Core ML model if the built-in label set lacks the event.
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Validation: test on representative recordings and background sounds before making reliability claims.
-
Product wording: report a sound event only; do not turn a classifier label into an unsupported claim about an animal’s emotional or medical state.
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