Audio Compression

Audio compression aims to reduce the size of audio files while preserving perceptual quality, a crucial task for efficient storage and transmission. Current research emphasizes neural network-based codecs, employing architectures like autoencoders, generative adversarial networks (GANs), and normalizing flows, often incorporating vector quantization and discrete token representations to achieve high compression ratios at low bitrates. These advancements are improving the fidelity of compressed audio across various domains (speech, music, environmental sounds) and enabling faster processing for applications such as large language model training and real-time audio communication. The resulting improvements in efficiency and quality have significant implications for diverse fields, including telecommunications, music production, and speech recognition.