Polyphonic Sound

Polyphonic sound, encompassing the simultaneous occurrence of multiple sound events, presents a significant challenge in audio processing. Current research focuses on improving the accuracy and efficiency of sound event detection (SED) and separation in polyphonic mixtures, employing deep learning models such as convolutional recurrent neural networks (CRNNs), transformers, and capsule networks, often incorporating attention mechanisms and novel feature representations like frequency dynamic convolutions. These advancements are crucial for applications ranging from environmental monitoring and music transcription to assistive technologies, driving improvements in the robustness and performance of audio analysis systems. The development of robust evaluation metrics, such as the polyphonic sound detection score (PSDS), is also a key area of ongoing research.