<![CDATA[Efficient and stable audio feature extraction is essential for real-time AI systems, yet conventional spectral representations often incur preprocessing overhead and framework-conversion dependencies. This study proposes PATSE (Polarized Amplitude Time Spiral Encoding), a PyTorch-native spatial–temporal encoding framework that converts one-dimensional audio into CNN-compatible two-dimensional polar representations while preserving temporal–amplitude information. Evaluated on 100 infant cry samples, PATSE achieved competitive CPU runtime (5.37 ms), improved latency stability over Mel-Spectrogram, and 91% classification accuracy with a Macro-F1 score of 0.85 using a lightweight CNN. These results suggest that PATSE provides an effective balance between computational efficiency, framework integration, and discriminative representation in the evaluated CPU-based infant cry setting, while broader validation is reserved for future work.]]>
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