In this paper, we address the problem of direction of arrival (DOA)
estimation for multiple targets in the presence of sensor failures in a sparse
array. Generally, sparse arrays are known with very high-resolution
capabilities, where N physical sensors can resolve up to $\mathcal{O}(N^2)$
uncorrelated sources. However, among the many configurations introduced in the
literature, the arrays that provide the largest hole-free co-array are the most
susceptible to sensor failures. We propose here two machine learning (ML)
methods to mitigate the effect of sensor failures and maintain the DOA
estimation performance and resolution. The first method enhances the
conventional spatial smoothing using deep neural network (DNN), while the
second one is an end-to-end data-driven method. Numerical results show that
both approaches can significantly improve the performance of MRA with two
failed sensors. The data-driven method can maintain the performance of the
array with no failures at high signal-tonoise ratio (SNR). Moreover, both
approaches can even perform better than the original array at low SNR thanks to
the denoising effect of the proposed DNN

本文针对稀疏阵列感测器失效问题，在神经网络和数据驱动方法中使用两种机器学习方法以提高 MRA 的性能，实验结果表明两种方法均可显著改善阵列的性能。

深度学习辅助下的鲁棒稀疏阵列雷达

Resilient Sparse Array Radar with the Aid of Deep Learning

While direction of arrival (DOA) of sound events is generally estimated from
multichannel audio data recorded in a microphone array, sound events usually
derive from visually perceptible source objects, e.g., sounds of footsteps come
from the feet of a walker. This paper proposes an audio-visual sound event
localization and detection (SELD) task, which uses multichannel audio and video
information to estimate the temporal activation and DOA of target sound events.
Audio-visual SELD systems can detect and localize sound events using signals
from a microphone array and audio-visual correspondence. We also introduce an
audio-visual dataset, Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23),
which consists of multichannel audio data recorded with a microphone array,
video data, and spatiotemporal annotation of sound events. Sound scenes in
STARSS23 are recorded with instructions, which guide recording participants to
ensure adequate activity and occurrences of sound events. STARSS23 also serves
human-annotated temporal activation labels and human-confirmed DOA labels,
which are based on tracking results of a motion capture system. Our benchmark
results show that the audio-visual SELD system achieves lower localization
error than the audio-only system. The data is available at
this https URL

本文提出了一个音频 - 视频声音事件本地化和检测（SELD）任务，它使用多通道音频和视频信息来估计目标声音事件的时间激活和 DOA。音频 - 视觉 SELD 系统可以使用来自麦克风阵列和音频 - 视觉对应的信号来检测和定位声音事件，并介绍了一个音频 - 视觉数据集，其中包含了用于监测人员活动和声音事件发生的多通道音频数据记录。

STARSS23: 具有声音事件的时空注释的真实场景空间录音的音频 - 视觉数据集

STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes  with Spatiotemporal Annotations of Sound Events

Sound event detection (SED) and localization refer to recognizing sound
events and estimating their spatial and temporal locations. Using neural
networks has become the prevailing method for SED. In the area of sound
localization, which is usually performed by estimating the direction of arrival
(DOA), learning-based methods have recently been developed. In this paper, it
is experimentally shown that the trained SED model is able to contribute to the
direction of arrival estimation (DOAE). However, joint training of SED and DOAE
degrades the performance of both. Based on these results, a two-stage
polyphonic sound event detection and localization method is proposed. The
method learns SED first, after which the learned feature layers are transferred
for DOAE. It then uses the SED ground truth as a mask to train DOAE. The
proposed method is evaluated on the DCASE 2019 Task 3 dataset, which contains
different overlapping sound events in different environments. Experimental
results show that the proposed method is able to improve the performance of
both SED and DOAE, and also performs significantly better than the baseline
method.

本文提出基于神经网络的声音事件检测和定位方法，该方法训练得到的声音事件检测模型可以帮助到方位角的估计，同时也能够显著提高声音事件检测和方位角估计的性能。