bioRxiv / medRxiv
open access
CC BY 4.0
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- DFD-Lab: A Modular Audio-Visual Deepfake Detection Pipeline#1050022
- Interpreting Dolphin Vocal Sequences via Multiple Sequence Alignment#986430
- AudioFuse: Unified Spectral-Temporal Learning via a Hybrid ViT-1D CNN Architecture for Robust Phonocardiogram Classification#972962
- More than a whistle: automated detection of marine sound sources with a convolutional neural network#382972
- BUT Systems and Analyses for the ASVspoof 5 Challenge#972368
Record · ID 1227686
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive