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README.md

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-# TAC
+# End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation
+
+This repository provides the model implementation and dataset generation scripts for the paper "End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation" by Yi Luo, Zhuo Chen, Nima Mesgarani and Takuya Yoshioka. The paper introduces ***transform-average-concatenate (TAC)***, a simple module to allow end-to-end multi-channel separation systems to be invariant to microphone permutation (indexing) and number. Although designed for ad-hoc array configuration, TAC also provides significant performance improvement in fixed geometry microphone configuration, showing that it can serve as a general design paradigm for end-to-end multi-channel processing systems.
+
+## Model
+
+We implement TAC in the framework of ***filter-and-sum network (FaSNet)***, a recently proposed multi-channel speech separation model operated in time-domain. FaSNet is a neural beamformer that performs the standard filter-and-sum beamforming in time domain, while the beamforming coefficients are estimated by a neural network in an end-to-end fashion. Here we provide the original FaSNet implementation and the modified FaSNet with TAC applied. For details please refer to the paper.
+
+The building blocks in the FaSNet models are ***dual-path RNNs (DPRNNs)***, a simple yet effective method for organizing RNN layers to allow successful modeling of extremely long sequential data. For details about DPRNN please refer to ["Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation"](https://arxiv.org/abs/1910.06379). The implementation of DPRNN, as well as the combination of DPRNN and TAC, can be found in [*utility/models*](https://github.com/yluo42/TAC/blob/master/utility/models.py).
+
+## Dataset
+
+The evaluation of the model is on both ad-hoc array and fixed geometry array configurations. For data generation please refer to the *data* folder.