Audio Spectrogram

Audio spectrograms are visual representations of sound, mapping frequency content over time, crucial for analyzing and processing audio signals. Current research focuses on improving spectrogram-based models for various tasks, including speech recognition, sound classification, and audio synthesis, employing architectures like convolutional neural networks, transformers, and diffusion models, often incorporating techniques like masked modeling and attention mechanisms. These advancements lead to more efficient and accurate systems in diverse applications such as healthcare monitoring, manufacturing quality control, and music information retrieval. The development of optimized spectrograms and associated algorithms is driving progress in numerous audio-related fields.