Audio Classification — From k-NN on MFCCs to AST and BEATs
Everything from "dog barking vs siren" to "which language is this" is audio classification. The features are mels. The architecture moves each decade. The evaluation stays AUC, F1, and per-class recall.
You get a 10-second clip. You want to know: "what is it?" Urban sound (siren, drill, dog), speech command (yes/no/stop), language ID (en/es/ar), speaker emotion (angry/neutral), or environmental sound (indoor/outdoor, babble). All of these are audio classification, and in 2026 the baseline architecture is mature: log-mel → CNN or Transformer → softmax.
The core difficulty is not the network. It is data. Audio datasets have brutal class imbalance, strong domain shift (clean vs noisy), and label noise (who decided "urban babble" vs "restaurant noise"?). The 80% of the problem is curation, augmentation, and evaluation, not swapping CNN for Transformer.
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