Lightweight separation-based multi-event perception for distributed acoustic sensing

Abstract Phase-sensitive optical time-domain reflectometry (Φ-OTDR) is widely used for distributed acoustic sensing, yet effective event recognition in multi-source interference environments remains a significant challenge. To address these issues, this paper proposes a lightweight and efficient separation framework based on the time-frequency interleaved gain extraction and reconstruction (TIGER) architecture. A uniform band-split strategy replaces the speech-oriented frequency allocation, while multi-scale processing and attention model the temporal and spectral structure of the mixtures. Experiments cover six event classes, two-source and three-source synthetic mixtures, and real simultaneous two-source recordings. Comparisons include ConvTasNet Base and Tiny, DPRNN, SuDoRM-RF, TF-GridNet, and TIGER with the adapted original band split. Uniform-band TIGER achieves per-source classification accuracies of 97.17 ± 0.16% and 94.74 ± 0.01% with 0.822 M and 0.779 M parameters in the two-source and three-source tasks, respectively. Relative to the original-band variant, it improves accuracy by 2.29 and 1.79 percentage points and SI-SDR by 0.509 and 0.430 dB. In the three-source task, TIGER uses 9.5% of the parameters of TF-GridNet, with an accuracy difference of 0.87 percentage points. On 21 real two-source windows, it achieves an 87.30 ± 2.75% both-events-correct rate despite measurable differences between real and synthetic input features. Repeated training and paired test-mixture analysis support the performance comparisons. On an RTX 4090, the three-source separator processes a 0.5 s input in 73.69 ms, giving a real-time factor of 0.147. The results establish uniform-band adaptation as an effective approach to compact separation-based multi-event perception.