This paper focuses on room fingerprinting, a task involving the analysis of
an audio recording to determine the specific volume and shape of the room in
which it was captured. While it is relatively straightforward to determine the
basic room parameters from the Room Impulse Responses (RIR), doing so from a
speech signal is a cumbersome task. To address this challenge, we introduce a
dual-encoder architecture that facilitates the estimation of room parameters
directly from speech utterances. During pre-training, one encoder receives the
RIR while the other processes the reverberant speech signal. A contrastive loss
function is employed to embed the speech and the acoustic response jointly. In
the fine-tuning stage, the specific classification task is trained. In the test
phase, only the reverberant utterance is available, and its embedding is used
for the task of room shape classification. The proposed scheme is extensively
evaluated using simulated acoustic environments.

该论文研究了室内指纹识别，通过分析音频记录以确定录制音频时所在房间的音量和形状。提出了一种双编码器架构，可直接从语音中估算房间参数，通过对比损失函数将语音和声学响应联合嵌入，通过在预训练和微调阶段的训练实现具体的分类任务，并在测试阶段使用嵌入来进行房间形状分类。该方案在模拟声学环境中进行了广泛评估。

RevRIR: 利用对比学习进行联合混响语音和室内冲激响应嵌入，应用于房间形状分类

RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding  using Contrastive Learning with Application to Room Shape Classification

The acoustic environment can degrade speech quality during communication
(e.g., video call, remote presentation, outside voice recording), and its
impact is often unknown. Objective metrics for speech quality have proven
challenging to develop given the multi-dimensionality of factors that affect
speech quality and the difficulty of collecting labeled data. Hypothesizing the
impact of acoustics on speech quality, this paper presents MOSRA: a
non-intrusive multi-dimensional speech quality metric that can predict room
acoustics parameters (SNR, STI, T60, DRR, and C50) alongside the overall mean
opinion score (MOS) for speech quality. By explicitly optimizing the model to
learn these room acoustics parameters, we can extract more informative features
and improve the generalization for the MOS task when the training data is
limited. Furthermore, we also show that this joint training method enhances the
blind estimation of room acoustics, improving the performance of current
state-of-the-art models. An additional side-effect of this joint prediction is
the improvement in the explainability of the predictions, which is a valuable
feature for many applications.

本文介绍了一种非侵入式的多维度语音质量度量标准 MOSRA，可以预测房间声学参数，通过显式优化模型来学习这些参数，并同时改善 MOS 设备的泛化能力和盲预测房间声学的性能。