Is FFT window length a neutral preprocessing choice in CNN-based dolphin whistle detection? A cross-domain sensitivity analysis.

Convolutional neural networks (CNNs) operating on spectrogram images are the established method for automated cetacean whistle detection in passive acoustic monitoring (PAM). The FFT window length (N_fft) used during spectrogram generation is routinely fixed without justification, yet it determines the time-frequency resolution and, consequently, how the resulting image is distorted when resized to a fixed CNN input dimension. This study presents a controlled sensitivity analysis of N_fft across five values (128, 256, 512, 1024, 2048) on binary Tursiops truncatus whistle detection, using 10-fold cross-validation on an in-domain dataset (Oltremare, 192 kHz) and cross-domain evaluation on an independent open-ocean benchmark (DCLDE 2022). All experiments were conducted within a formally defined, open-source pipeline (ai-pam-pipeline). In-domain performance is uniformly high across all configurations. Cross-domain results diverge: N_fft = 256 significantly outperforms 512, 1024, and 2048 in macro F1, while maintaining a false discovery rate (FDR) of exactly zero across all 10 folds and all tested classification thresholds. N_fft = 128 achieves comparable recall but produces FDR > 0 in all 10 folds, exhibiting a transfer failure that is undetectable by in-domain validation. These results show that a preprocessing parameter routinely treated as an implementation detail has a large, systematic effect on cross-domain generalization. The study does not aim to propose N_fft = 256 as a universal optimum; rather, it tests the common assumption that this parameter is neutral and safe under a fully specified representation regime, and demonstrates that it is not.