<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wang, Yihan</style></author><author><style face="normal" font="default" size="100%">Qian, Yufan</style></author><author><style face="normal" font="default" size="100%">Qingbo Huang</style></author><author><style face="normal" font="default" size="100%">Tianshu Qu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Parametric Dual-Channel Audio Coding via Learned Time-Frequency Masking</style></title><secondary-title><style face="normal" font="default" size="100%">the AES 160th Convention</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">28-30 May </style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://aes.org/publications/elibrary-page/?id=23191</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Copenhagen, Denmark</style></pub-location><pages><style face="normal" font="default" size="100%">10294</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>