# Unsupervised training of neural mask-based beamforming

## TL;DR

This paper introduces an unsupervised neural network training method for mask-based beamforming that does not require parallel clean data or pre-trained teachers, enabling training directly on real noisy recordings.

## Contribution

It presents a novel unsupervised training approach for neural mask estimators in beamforming, eliminating the need for pre-trained models or simulated data.

## Key findings

- Achieves speech recognition performance comparable to supervised systems.
- Effective training on real noisy recordings without clean references.
- Demonstrates robustness across noise and reverberation conditions.

## Abstract

We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.