Audio Modeling

Audio modeling focuses on developing computational methods to represent, generate, manipulate, and understand audio signals. Current research emphasizes efficient generative models, such as masked autoencoders and diffusion models, often incorporating techniques from image processing like vector quantization and adversarial training, to achieve high-fidelity audio synthesis and compression. These advancements are driven by the need for improved audio datasets and the development of novel architectures like causal transformers to better capture temporal dependencies in audio data. The resulting improvements in audio generation, compression, and classification have significant implications for applications ranging from text-to-speech synthesis and virtual reality to music production and audio-based assistive technologies.