Practical Solutions and Value of Source-Disentangled Neural Audio Codec (SD-Codec)
Revolutionizing Audio Compression
Neural audio codecs convert audio signals into tokens, improving compression efficiency without compromising quality.
Challenges Addressed
Existing models struggle to differentiate between different audio domains, hindering effective data modeling and sound production.
Introducing SD-Codec
SD-Codec combines source separation and audio coding to classify audio signals into distinct domains, enhancing audio quality and control.
Benefits of SD-Codec
Improves interpretability of neural audio codecs, enables precise audio manipulation, and enhances audio resynthesis quality.
Key Contributions
SD-Codec extracts distinct audio sources, utilizes shared residual vector quantization effectively, and performs well in source separation and reconstruction.
Advancements in Audio Production
SD-Codec offers a more advanced and manageable method of audio production and compression, revolutionizing the field of neural audio codecs.
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