@conference {41101,
	title = {A Parametric Dual-Channel Audio Coding via Learned Time-Frequency Masking},
	booktitle = {the AES 160th Convention},
	year = {2026},
	month = {28-30 May },
	pages = {10294},
	address = {Copenhagen, Denmark},
	abstract = {While Neural Audio Codecs (NAC) have revolutionized monaural audio compression, achieving high-fidelity dual-channel coding at low bitrates remains a significant challenge. Existing approaches often rely on naive independent channel quantization, leading to phase incoherence, or entangled latent modeling, which sacrifices spatial precision for spectral energy. This paper proposes a novel dual-channel coding framework based on contentspatial disentanglement. Reframing spatial reconstruction as an informed source separation task, our architecturesynergizes a frozen, pre-trained DAC encoder for robust mono content preservation with a parameter-efficient side information encoder that predicts fine-grained time-frequency masks. To ensure precise spatial imaging, we introduce explicit physical constraints into the end-to-end training. Experimental results indicate that at low bitrates of 9 and 11 kbps, the proposed method outperforms state-of-the-art dual-mono neural baselines and industry standards in both objective spatial metrics and subjective MUSHRA evaluations.},
	url = {https://aes.org/publications/elibrary-page/?id=23191},
	author = {Wang, Yihan and Qian, Yufan and Qingbo Huang and Tianshu Qu}
}
