Sound event localization and detection (SELD) combines two subtasks: sound event detection (SED) and direction of arrival (DOA) estimation. SELD is usually tackled as an audio-only problem, but visual information has been recently included. Few audio-visual (AV)-SELD works have been published and most employ vision via face/object bounding boxes, or human pose keypoints. In contrast, we explore the integration of audio and visual feature embeddings extracted with pre-trained deep networks. For the visual modality, we tested ResNet50 and Inflated 3D ConvNet (I3D). Our comparison of AV fusion methods includes the AV-Conformer and Cross-Modal Attentive Fusion (CMAF) model. Our best models outperform the DCASE 2023 Task3 audio-only and AV baselines by a wide margin on the development set of the STARSS23 dataset, making them competitive amongst state-of-the-art results of the AV challenge, without model ensembling, heavy data augmentation, or prediction post-processing. Such techniques and further pre-training could be applied as next steps to improve performance.

Audio-visual video parsing is the task of categorizing a video with weak labels at the segment level, and predicting them as audible or visible events. Recent methods have leveraged the attention mechanism to capture the semantic correlations among the whole video across the audio-visual modalities. However, these approaches may overlook the importance of individual segments and their interrelations within a video, typically relying on a single modality when learning features. In this paper, we propose a novel interactive-enhanced cross-modal perception method (CM-PIE), which can learn fine-grained features by applying a segment-based attention module. In addition, a cross-modal aggregation block is introduced to jointly optimize the semantic representation of audio and visual signals by enhancing inter-modal interactions. Experimental results show that our model offers improved parsing performance on the Look, Listen, and Parse (LLP) dataset compared to other methods.

Particle flow (PF) is a method originally proposed for single target tracking, and used recently to address the weight degeneracy problem of the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter for audio-visual (AV) multi-speaker tracking, where the particle flow is calculated by using only the measurements near the particle, assuming that the target is detected, as in a recent method based on non-zero particle flow (NPF), i.e. the AV-NPF-SMC-PHD filter. This, however, can be problematic when occlusion happens and the occluded speaker may not be detected. To address this issue, we propose a new method where the labels of the particles are estimated using the likelihood function, and the particle flow is calculated in terms of the selected particles with the same labels. As a result, the particles associated with detected speakers and undetected speakers are distinguished based on the particle labels. With this novel method, named as AV-LPF-SMC-PHD, the speaker states can be estimated as the weighted mean of the labelled particles, which is computationally more efficient than using a clustering method as in the AV-NPF-SMC-PHD filter. The proposed algorithm is compared systematically with several baseline tracking methods using the AV16.3, AVDIAR and CLEAR datasets, and is shown to offer improved tracking accuracy with a lower computational cost.

Intensity Particle Flow (IPF) SMC-PHD has been proposed recently for multi-target tracking. In this paper, we extend IPF-SMC-PHD filter to distributed setting, and develop a novel consensus method for fusing the estimates from individual sensors, based on Arithmetic Average (AA) fusion. Different from conventional AA method which may be degraded when unreliable estimates are presented, we develop a novel arithmetic consensus method to fuse estimates from each individual IPF-SMC-PHD filter with partial consensus. The proposed method contains a scheme for evaluating the reliability of the sensor nodes and preventing unreliable sensor information to be used in fusion and communication in sensor network, which help improve fusion accuracy and reduce sensor communication costs. Numerical simulations are performed to demonstrate the advantages of the proposed algorithm over the uncooperative IPF-SMC-PHD and distributed particle-PHD with AA fusion.

Audio-visual tracking of multiple speakers requires to estimate the state (e.g. velocity and location) of each speaker by leveraging the information of both audio and visual modalities. Estimating the number of speakers and their states jointly remains a challenging problem. We propose an Audio-Visual Possion Multi-Bernoulli Mixture Filter (AV-PMBM) that can not only predict the number of speakers but also give accurate estimation of their states. We also propose a novel sound source localization technique based on DOA information and a deep learning based object detector to provide reliable audio measurements for the AV tracker. To our knowledge, this represents the first attempt using PMBM for multi-speaker tracking with audio visual modalities. Experiments on the AV16.3 dataset demonstrate that AV-PMBM achieves state-of-the-art performance in optimal sub-pattern assignment (OSPA).