The proposed CS-RVQ approach can be extended to handle more diverse audio types beyond speech by adapting the codec's architecture and training process to accommodate the unique characteristics of different audio signals. For music, which often contains more complex and varied patterns, the codebook design and quantization process can be optimized to capture the nuances and intricacies of musical notes and instruments. This may involve increasing the number of codebooks or adjusting the vector quantization process to better represent the frequency and timbre variations present in music.
For environmental sounds, which can have a wide range of frequencies and textures, the CS-RVQ approach can be enhanced by incorporating additional features or preprocessing steps to extract relevant information from the audio signals. This could include incorporating spectrogram analysis techniques or leveraging domain-specific knowledge to improve the representation and quantization of environmental sounds.
By tailoring the CS-RVQ approach to the specific characteristics of different audio types, such as music and environmental sounds, the codec can achieve higher compression efficiency and audio quality across a broader range of audio signals.