Libro

Detecting bird species from environmental acoustic signals presents a significant problem of noise interference, which diminishes the quality of extracted features and limits model reliability. To address this problem, a spectro-temporal deep CNN–BiLSTM framework is proposed here for recognizing bird species from environmental audio data with a solution in advancement in existing approaches for effectively modeling both local spectral and sequential temporal patterns. The CNN component extracts discriminative spectral features, while the BiLSTM layer captures long-term temporal dependencies, preserving the dynamics of bird calls in acoustically complex environments.