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。