A Multi-Stage Separation-and-Classification Framework Guided by Complementary Acoustic-to-Semantic Clues
Year: 2026
Type: preprint
Abstract: This report describes the system proposed for the DCASE 2026 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes (S5). Specifically, we develop a multi-stage framework in which each stage couples a separation model with a classification model. The first stage performs source separation and classification directly on the multi-channel mixture. Its outputs are then propagated to the following stage as two complementary clues that progressively refine each target estimate: (i) an enrollment clue, the separated waveform itself, serving as a low-level acoustic reference; and (ii) a class clue, the predicted label encoded as a one-hot vector. The third stage reuses the second-stage outputs under the same scheme, forming an iterative self-guided refinement process. In addition, we use a fine-grained frame-level audio embedding from an audio encoder pretrained on a large audio corpus as an additional clue to further improve the audio separation performance. On the test set, the proposed system achieves a CAPI-SDRi of 15.51 dB, a mixture accuracy of 71.09\%, and a source accuracy of 78.62\%; with an improvement of 7.02 dB, 10.38\%p and 8.22\%p compared with the challenge baseline, respectively.
Source: arXiv (Cornell University)
Institution: Korea Advanced Institute of Science and Technology
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7165758384
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