Discrete Audio Representation

Discrete audio representation, or audio tokenization, aims to represent audio signals as sequences of discrete units, analogous to words in text, enabling the application of powerful language modeling techniques to audio. Current research focuses on developing efficient tokenization methods, often based on vector quantization, and integrating these representations into transformer-based models for tasks like music generation, speech recognition, and image-to-audio synthesis. This approach offers potential for significant compression of audio data while maintaining performance comparable to traditional methods like mel-spectrograms, leading to improved efficiency in various applications and facilitating the development of more sophisticated audio processing systems.