Audio Detection

Audio detection research focuses on accurately identifying and classifying sounds, encompassing diverse applications from speaker diarization and music deepfake detection to environmental monitoring and child safety systems. Current efforts concentrate on developing robust models, often employing deep learning architectures like convolutional neural networks (CNNs) and transformers, that can generalize across varied datasets and handle challenges such as partial spoofing and limited training data. These advancements have significant implications for various fields, improving the accuracy and efficiency of audio-based applications while addressing concerns about misinformation and malicious use of synthesized audio.