Recording Device

Recording device variability significantly impacts the performance of audio processing systems like sound event classification and acoustic scene classification. Current research focuses on mitigating this issue through techniques such as data augmentation (e.g., using CycleGANs to simulate different devices), frequency-wise normalization of spectrograms, and the development of robust neural network architectures (including transformer and inception-residual networks). These advancements aim to improve the generalization and robustness of audio analysis models, leading to more reliable applications in areas such as environmental monitoring, assistive technologies, and human-computer interaction.