# Neural Network-based Virtual Microphone Estimator

Source: [Neural Network-based Virtual Microphone Estimator](<https://www.sota2.com/research/paper/68fa690e1e209131cea1fcf2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:51:52.067Z

Authors: Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani, Rintaro Ikeshita, Keisuke Kinoshita, Shoko Araki

Published: 2021-01-12T06:30:24.000Z

## Abstract

Developing microphone array technologies for a small number of microphones is important due to the constraints of many devices. One direction to address this situation consists of virtually augmenting the number of microphone signals, e.g., based on several physical model assumptions. However, such assumptions are not necessarily met in realistic conditions. In this paper, as an alternative approach, we propose a neural network-based virtual microphone estimator (NN-VME). The NN-VME estimates virtual microphone signals directly in the time domain, by utilizing the precise estimation capability of the recent time-domain neural networks. We adopt a fully supervised learning framework that uses actual observations at the locations of the virtual microphones at training time. Consequently, the NN-VME can be trained using only multi-channel observations and thus directly on real recordings, avoiding the need for unrealistic physical model-based assumptions. Experiments on the CHiME-4 corpus show that the proposed NN-VME achieves high virtual microphone estimation performance even for real recordings and that a beamformer augmented with the NN-VME improves both the speech enhancement and recognition performance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Omnidirectional Speech Enhancement on Omni-SE RM: 2ch, VM: 1ch](<https://www.sota2.com/research/sota/omnidirectional-speech-enhancement-on-omni-se-rm-2ch-vm-1ch.md>): Omnidirectional Speech Enhancement; Omni-SE RM: 2ch, VM: 1ch; SI-SDR (VM-BF): 3.37.

- [Omnidirectional Speech Enhancement on Omni-SE RM: 2ch, VM: 4ch](<https://www.sota2.com/research/sota/omnidirectional-speech-enhancement-on-omni-se-rm-2ch-vm-4ch.md>): Omnidirectional Speech Enhancement; Omni-SE RM: 2ch, VM: 4ch; SI-SDR (VM-BF): 3.69.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.04315>)

[PDF](<https://arxiv.org/pdf/2101.04315.pdf>)