# Direction of Arrival Correction through Speech Quality Feedback

arXiv: https://arxiv.org/abs/2408.07234 · HTML: https://mlsanity.com/paper/2408.07234/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 9 of 9)

- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [MOSNet: Deep Learning based Objective Assessment for Voice Conversion](https://mlsanity.com/paper/1904.08352/) (2019)
- [TorchAudio: Building Blocks for Audio and Speech Processing](https://mlsanity.com/paper/2110.15018/) (2021)
- [TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudio](https://mlsanity.com/paper/2304.01448/) (2023)
- [Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model based on BLSTM](https://mlsanity.com/paper/1808.05344/) (2018)
- [A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals](https://mlsanity.com/paper/2007.15797/) (2020)
- [A ROS2 based communication architecture for control in collaborative and intelligent automation systems](https://mlsanity.com/paper/1905.09654/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-agent Auditory Scene Analysis](https://mlsanity.com/paper/2507.02755/) (2025)
- [Multi-agent Auditory Scene Analysis: Improved Localization Speed and Robustness by Multi-beamformed Speech Quality Feedback](https://mlsanity.com/paper/2610.00538/) (2026)

## Abstract (arXiv)

Real-time speech enhancement has began to rise in performance, and the Demucs Denoiser model has recently demonstrated strong performance in multiple-speech-source scenarios when accompanied by a location-based speech target selection strategy. However, it has shown to be sensitive to errors in the direction-of-arrival (DOA) estimation. In this work, a DOA correction scheme is proposed that uses the real-time estimated speech quality of its enhanced output as the observed variable in an Adam-based optimization feedback loop to find the correct DOA. In spite of the high variability of the speech quality estimation, the proposed system is able to correct in real-time an error of up to 15o using only the speech quality as its guide. Several insights are provided for future versions of the proposed system to speed up convergence and further reduce the speech quality estimation variability.