TIGER: Time frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation Mohan Xu , Kai Li , Guo Chen, Xiaolin Hu Tsinghua University, Beijing, China Equal contribution 📜 ICLR 2025 🎶 Demo 🤗 Dataset TIGER is a lightweight model for speech separation which effectively extracts key acoustic features through frequency band split, multi scale and full frequency frame modeling. 💥 News [2025 01 23] We release the code and pre trained model of TIGER! 🚀 [2025 01 23] We release the TIGER model and the EchoSet dataset! 🚀 📜 Abstract In this paper, we propose a speech separation model with significantly reduced parameter size and computational cost: Time Frequency Interleaved Gain Extraction and Reconstruction Network (TIGER). TIGER leverages prior knowledge to divide frequency bands and applies compression on frequency information. We employ a multi scale selective attention (MSA) module to extract contextual features, while introducing a full frequency frame attention (F^3A) module to capture both temporal and frequency contextual information. Additionally, to more realistically evaluate the performance of speech separation models in complex acoustic environ…
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