A Practical Audio Preprocessing Approach for Multi-Species Sound Classification

We present an audio-preprocessing pipeline that boosts classification performance for multi-species sound identification in Colombian soundscapes. Developed for BirdCLEF 2025 and evaluated on recordings from Reserva Natural El Silencio (Magdalena Medio Valley), the pipeline isolates vocalizations, removes silence, and filters noise to produce cleaner BirdNET embeddings. We train an MLP on both the raw and balanced datasets, and used BirdNET as the baseline architecture trained on the balanced dataset. Results in multi-taxon species classification show that improving signal quality can offset model complexity, where a cleaned-input MLP matches or surpasses the baseline with modest compute. This underscores the value of preprocessing for bioacoustic monitoring in noisy, resource-limited settings and demonstrates that robust baselines can be built with accessible computing resources common in biodiversity-rich developing countries.