# Expectation-Maximization for Speech Source Separation Using Convolutive Transfer Function

## TL;DR

This paper introduces an EM-based method for speech source separation in reverberant environments using convolutive transfer functions in the STFT domain, improving accuracy over narrowband models.

## Contribution

It proposes a novel joint estimation approach of CTF coefficients and source signals using EM, enhancing separation performance in reverberant conditions.

## Key findings

- Effective in highly reverberant environments
- Outperforms narrowband models
- Provides accurate room filter estimation

## Abstract

This paper addresses the problem of under-determinded speech source separation from multichannel microphone singals, i.e. the convolutive mixtures of multiple sources. The time-domain signals are first transformed to the short-time Fourier transform (STFT) domain. To represent the room filters in the STFT domain, instead of the widely-used narrowband assumption, we propose to use a more accurate model, i.e. the convolutive transfer function (CTF). At each frequency band, the CTF coefficients of the mixing filters and the STFT coefficients of the sources are jointly estimated by maximizing the likelihood of the microphone signals, which is resolved by an Expectation-Maximization (EM) algorithm. Experiments show that the proposed method provides very satisfactory performance under highly reverberant environments.