Neural Codec

Neural codecs are deep learning models designed to efficiently compress and decompress audio and video signals into discrete token representations, improving upon traditional methods in rate-distortion performance. Current research focuses on enhancing model architectures (e.g., transformers, convolutional networks) to improve compression ratios, addressing issues like recency bias and preserving crucial information such as emotional content or spatial cues in audio, and achieving real-time performance on resource-constrained devices. These advancements have significant implications for various applications, including text-to-speech synthesis, speech enhancement, video compression, and security-sensitive tasks like speaker verification, by enabling higher quality and more efficient data transmission and processing.