The ability to generalize to a wide range of recording devices is a crucial
performance factor for audio classification models. The characteristics of
different types of microphones introduce distributional shifts in the digitized
audio signals due to their varying frequency responses. If this domain shift is
not taken into account during training, the model's performance could degrade
severely when it is applied to signals recorded by unseen devices. In
particular, training a model on audio signals recorded with a small number of
different microphones can make generalization to unseen devices difficult. To
tackle this problem, we convolve audio signals in the training set with
pre-recorded device impulse responses (DIRs) to artificially increase the
diversity of recording devices. We systematically study the effect of DIR
augmentation on the task of Acoustic Scene Classification using CNNs and Audio
Spectrogram Transformers. The results show that DIR augmentation in isolation
performs similarly to the state-of-the-art method Freq-MixStyle. However, we
also show that DIR augmentation and Freq-MixStyle are complementary, achieving
a new state-of-the-art performance on signals recorded by devices unseen during
training.

通过使用预先录制的设备脉冲响应（DIR）对训练集中的音频信号进行卷积来人为增加录音设备的多样性，以解决大多数音频分类模型在泛化到不同设备录制的信号时的性能不佳问题，并成功在 Acoustic Scene Classification 任务上实现了新的最高性能。

通过脉冲响应增强实现设备鲁棒的声景分类

Device-Robust Acoustic Scene Classification via Impulse Response  Augmentation

This paper investigates the accuracy of various Generalized Cross-Correlation
with Phase Transform (GCC-PHAT) methods for a close pair of microphones. We
investigate interpolation-based methods and also propose another approach based
on Singular Value Decomposition (SVD). All investigated methods are implemented
in C code, and the execution time is measured to determine which approach is
the most appealing for real-time applications on low-cost embedded hardware.

本文研究了各种广义互相关相位变换（GCC-PHAT）方法在一对近距离麦克风中的准确性。我们探讨了基于插值的方法，还提出了一种基于奇异值分解（SVD）的方法。所有研究方法均采用 C 代码实现，并测量执行时间以确定哪种方法最适合于低成本嵌入式硬件上的实时应用。

基于 GCC-PHAT 的近距离麦克风阵列方向估计方法的复杂度和准确性研究

A Study of the Complexity and Accuracy of Direction of Arrival  Estimation Methods Based on GCC-PHAT for a Pair of Close Microphones

The hearing sense on a mobile robot is important because it is
omnidirectional and it does not require direct line-of-sight with the sound
source. Such capabilities can nicely complement vision to help localize a
person or an interesting event in the environment. To do so the robot auditory
system must be able to work in noisy, unknown and diverse environmental
conditions. In this paper we present a robust sound source localization method
in three-dimensional space using an array of 8 microphones. The method is based
on time delay of arrival estimation. Results show that a mobile robot can
localize in real time different types of sound sources over a range of 3 meters
and with a precision of 3 degrees.

本文介绍使用 8 个麦克风的时间延迟到达估计法在三维空间中实现机器人对不同类型声源的实时定位，能够在 3 米范围内，3 度的精度内快速响应，以补充视觉对环境中感兴趣人或事件的本地化。