Sound event detection (SED) is an active area of audio research that aims to detect the temporal occurrence of sounds. In this paper, we apply SED to engine fault detection by introducing a multimodal SED framework that detects fine-grained engine faults of automobile engines using audio and accelerometer-recorded vibration. We first introduce the problem of engine fault SED on a dataset collected from a large variety of vehicles with expertly-labeled engine fault sound events. Next, we propose a SED model to temporally detect ten fine-grained engine faults that occur within vehicle engines and further explore a pretraining strategy using a large-scale weakly-labeled engine fault dataset. Through multiple evaluations, we show our proposed framework is able to effectively detect engine fault sound events. Finally, we investigate the interaction and characteristics of each modality and show that fusing features from audio and vibration improves overall engine fault SED capabilities.

A "match cut" is a common video editing technique where a pair of shots that have a similar composition transition fluidly from one to another. Although match cuts are often visual, certain match cuts involve the fluid transition of audio, where sounds from different sources merge into one indistinguishable transition between two shots. In this paper, we explore the ability to automatically find and create "audio match cuts" within videos and movies. We create a self-supervised audio representation for audio match cutting and develop a coarse-to-fine audio match pipeline that recommends matching shots and creates the blended audio. We further annotate a dataset for the proposed audio match cut task and compare the ability of multiple audio representations to find audio match cut candidates. Finally, we evaluate multiple methods to blend two matching audio candidates with the goal of creating a smooth transition. Project page and examples are available at: https://denfed.github.io/audiomatchcut/

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy.

Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.

In this paper we present AMPNet, an acoustic abnormality detection model deployed at ACV Auctions to automatically identify engine faults of vehicles listed on the ACV Auctions platform. We investigate the problem of engine fault detection and discuss our approach of deep-learning based audio classification on a large-scale automobile dataset collected at ACV Auctions. Specifically, we discuss our data collection pipeline and its challenges, dataset preprocessing and training procedures, and deployment of our trained models into a production setting. We perform empirical evaluations of AMPNet and demonstrate that our framework is able to successfully capture various engine anomalies agnostic of vehicle type. Finally we demonstrate the effectiveness and impact of AMPNet in the real world, specifically showing a 20.85% reduction in vehicle arbitrations on ACV Auctions' live auction platform.

Long-term surveillance applications often involve having to re-identify individuals over several days. The task is made even more challenging due to changes in appearance features such as clothing over a longitudinal time-span of days or longer. In this paper, we propose a novel approach called Bayesian Personalized-Wardrobe Model (BPWM) for long-term person re-identification (re-ID) by employing a Bayesian Personalized Ranking (BPR) for clothing features extracted from video sequences. In contrast to previous long-term person re-ID works, we exploit the fact that people typically choose their attire based on their personal preferences and that knowing a person’s chosen wardrobe can be used as a soft-biometric to distinguish identities in the long-term. We evaluate the performance of our proposed BP-WM on the extended Indoor Long-term Re-identification Wardrobe (ILRW) dataset. Experimental results show that our method achieves state-of-the-art performance and that BP-WM can be used as a reliable soft-biometric for person re-identification.

Deep metric learning leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminable embedding space. While effective sampling of pairs is critical for shaping the metric space during training, the relative interactions between pairs, and consequently the forces exerted on these pairs that direct their displacement in the embedding space can significantly impact the formation of well separated clusters. In this work, we identify a shortcoming of existing loss formulations which fail to consider more optimal directions of pair displacements as another criterion for optimization. We propose a novel direction regularization to explicitly account for the layout of sampled pairs and attempt to introduce orthogonality in the representations. The proposed regularization is easily integrated into existing loss functions providing considerable performance improvements. We experimentally validate our hypothesis on the Cars-196, CUB-200 and InShop datasets and outperform existing methods to yield state-of-the-art results on these datasets.

Learning robust representations for applications with multiple modalities of input can have a significant impact on its performance. Traditional representation learning methods rely on projecting the input modalities to a common subspace to maximize agreement amongst the modalities for a particular task. We propose a novel approach to representation learning that uses a latent representation decoder to reconstruct the target modality and thereby employs the target modality purely as a supervision signal for discovering correlations between the modalities. Through cross modality supervision, we demonstrate that the learnt representation is able to improve the performance of the task of facial action unit (AU) recognition when compared with the modality specific representations and even their fused counterparts. Our experiments on three AU recognition datasets - MMSE, BP4D and DISFA, show strong performance gains producing state-of-the-art results in spite of the absence of a modality.