Speaker diarization liumin

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• proceedings of the ureca@ntu ay2010-11 - Nanyang Technological ...
• 2021.5.24 CS papers
• Speech_Recognition
• MM '20: Proceedings of the 28th ACM International Conference on Multimedia - Part 2
• Accepted Papers: Main Conference
• Thank you for supporting arXiv
• New Graduate Degree Program Proposal Information Form
• Information Retrieval Technology
• Python speaker-change-detection Libraries

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proceedings of the ureca@ntu ay2010-11 - Nanyang Technological ...

Creative rhythmic transformations of musical audio refer to automated methods for manipulation of temporally-relevant sounds in time. This paper presents a method for joint synthesis and rhythm transformation of drum sounds through the use of adversarial autoencoders AAE. Users may navigate both the timbre and rhythm of drum patterns in audio recordings through expressive control over a low-dimensional latent space.

The model is based on an AAE with Gaussian mixture latent distributions that introduce rhythmic pattern conditioning to represent a wide variety of drum performances. The AAE is trained on a dataset of bar-length segments of percussion recordings, along with their clustered rhythmic pattern labels.

The decoder is conditioned during adversarial training for mixing of data-driven rhythmic and timbral properties. The system is trained with over bars from tracks in popular datasets covering various musical genres. In an evaluation using real percussion recordings, the reconstruction accuracy and latent space interpolation between drum performances are investigated for audio generation conditioned by target rhythmic patterns.

Such methods can be easily restricted by low-quality depth maps and redundant cross-modal features. Similar to the mechanism of visual color stage doctrine in human visual system, the proposed CMFM aims to explore the useful and important feature representations in feature response stage, and effectively integrate them into available cross-modal fusion features in adversarial combination stage.

Moreover, the proposed BMD learns the combination of cross-modal fusion features from multiple levels to capture both local and global information of salient objects and further reasonably boost the performance of the proposed method.

Comprehensive experiments demonstrate that the proposed method can achieve consistently superior performance over the other 14 state-of-the-art methods on six popular RGB-D datasets when evaluated by 8 different metrics.

As a very important research issue in digital media art, neural learning based video style transfer has attracted more and more attention. A lot of recent works import optical flow method to original image style transfer framework to preserve frame-coherency and prevent flicker. However, these methods highly rely on paired video datasets of content video and stylized video, which are often difficult to obtain. Another limitation of existing methods is that while maintaining inter-frame coherency, they will introduce strong ghosting artifacts.

In order to address these problems, this paper has following contributions: 1. Extensive experiments demonstrate that our method can produce natural and stable video frames with target style. Qualitative and quantitative comparisons also show that the proposed approach outperforms previous works in terms of overall image quality and inter-frame stability.

Video frame synthesis is an important task in computer vision and has drawn great interests in wide applications. However, existing neural network methods do not explicitly impose tensor low-rankness of videos to capture the spatiotemporal correlations in a high-dimensional space, while existing iterative algorithms require hand-crafted parameters and take relatively long running time. In this paper, we propose a novel multi-phase deep neural network Transform-Based Tensor-Net that exploits the low-rank structure of video data in a learned transform domain, which unfolds an Iterative Shrinkage-Thresholding Algorithm ISTA for tensor signal recovery.

Our design is based on two observations: i both linear and nonlinear transforms can be implemented by a neural network layer, and ii the soft-thresholding operator corresponds to an activation function. Further, such an unfolding design is able to achieve nearly real-time at the cost of training time and enjoys an interpretable nature as a byproduct. Anomaly detection in videos is commonly referred to as the discrimination of events that do not conform to expected behaviors. Most existing methods formulate video anomaly detection as an outlier detection task and establish normal concept by minimizing reconstruction loss or prediction loss on training data.

However, these methods performances suffer drops when they cannot guarantee either higher reconstruction errors for abnormal events or lower prediction errors for normal events. To avoid these problems, we introduce a novel contrastive representation learning task, Cluster Attention Contrast, to establish subcategories of normality as clusters.

Specifically, we employ multi-parallel projection layers to project snippet-level video features into multiple discriminate feature spaces.

Each of these feature spaces is corresponding to a cluster which captures distinct subcategory of normality, respectively. To acquire the reliable subcategories, we propose the Cluster Attention Module to draw thecluster attention representation of each snippet, then maximize the agreement of the representations from the same snippet under random data augmentations via momentum contrast.

In this manner, we establish a robust normal concept without any prior assumptions on reconstruction errors or prediction errors. Experiments show our approach achieves state-of-the-art performance on benchmark datasets. In the last years, the clothing industry has attracted a lot of interest from researchers. Increasing research efforts have been devoted into giving the buyer a way to improve the shopping experience by suggesting meaningful items to purchase.

These efforts result in works aiming at suggesting good matches for clothes, but seem to lack one important aspect: understanding the user's interest. In fact, to suggest something it is first necessary to collect the user's personal interests, or something about his or her previous purchases. Without this information, no personalized suggestion can be made. User interest understanding allows to recognize if a user is showing interest in a product he or she is looking at, acquiring precious information that can be later leveraged.

Usually user interest is associated to facial expressions, but these are known to be easily falsifiable. Moreover, when privacy is a concern, faces are often impossible to exploit. To address all these aspects, we propose an automatic system that aims to recognize the user's interest towards a garment by just looking at body posture and behaviour.

To train and evaluate our system we create a body pose interest dataset, named BodyInterest, which consists of 30 users looking at garments for a total of approximately 6 hours of videos. Extensive evaluations show the effectiveness of our proposed method.

Generally, adaptive bitrates for variable Internet bandwidths can be obtained through multi-pass coding. Referenceless prediction-based methods show practical benefits compared with multi-pass coding to avoid excessive computational resource consumption, especially in low-latency circumstances.

However, most of them fail to predict precisely due to the complex inner structure of modern codecs. Therefore, to improve the fidelity of prediction, we propose a referenceless prediction-based R-QP modeling PmR-QP method to estimate bitrate by leveraging a deep learning algorithm with only one-pass coding.

It refines the global rate-control paradigm in modern codecs on flexibility and applicability with few adjustments as possible. By exploring the potentials of bitstream and pixel features from the prerequisite of one-pass coding, it can reach the expectation of bitrate estimation in terms of precision. To be more specific, we first describe the R-QP relationship curve as a robust quadratic R-QP modeling function derived from the Cauchy-based distribution.

Second, we simplify the modeling function by fastening one operational point of the relationship curve received from the coding process. Third, we learn the model parameters from bitstream and pixel features, named them hybrid referenceless features, comprising texture information, hierarchical coding structure, and selected modes in intra-prediction. In this paper, we address self-supervised representation learning from human skeletons for action recognition.

Previous methods, which usually learn feature presentations from a single reconstruction task, may come across the overfitting problem, and the features are not generalizable for action recognition.

Instead, we propose to integrate multiple tasks to learn more general representations in a self-supervised manner. To realize this goal, we integrate motion prediction, jigsaw puzzle recognition, and contrastive learning to learn skeleton features from different aspects. Skeleton dynamics can be modeled through motion prediction by predicting the future sequence.

And temporal patterns, which are critical for action recognition, are learned through solving jigsaw puzzles. We further regularize the feature space by contrastive learning. Besides, we explore different training strategies to utilize the knowledge from self-supervised tasks for action recognition. We evaluate our multi-task self-supervised learning approach with action classifiers trained under different configurations, including unsupervised, semi-supervised and fully-supervised settings.

Domain adaptation aims to transfer knowledge from the source data with annotations to scarcely-labeled data in the target domain, which has attracted a lot of attention in recent years and facilitated many multimedia applications.

Recent approaches have shown the effectiveness of using adversarial learning to reduce the distribution discrepancy between the source and target images by aligning distribution between source and target images at both image and instance levels.

However, this remains challenging since two domains may have distinct background scenes and different objects. Moreover, complex combinations of objects and a variety of image styles deteriorate the unsupervised cross-domain distribution alignment. To address these challenges, in this paper, we design an end-to-end approach for unsupervised domain adaptation of object detector. Specifically, we propose a Multi-level Entropy Attention Alignment MEAA method that consists of two main components: 1 Local Uncertainty Attentional Alignment LUAA module to accelerate the model better perceiving structure-invariant objects of interest by utilizing information theory to measure the uncertainty of each local region via the entropy of the pixel-wise domain classifier and 2 Multi-level Uncertainty-Aware Context Alignment MUCA module to enrich domain-invariant information of relevant objects based on the entropy of multi-level domain classifiers.

The proposed MEAA is evaluated in four domain-shift object detection scenarios. Experiment results demonstrate state-of-the-art performance on three challenging scenarios and competitive performance on one benchmark dataset. Estimating the 3D hand pose from a monocular RGB image is important but challenging. A solution is training on large-scale RGB hand images with accurate 3D hand keypoint annotations. However, it is too expensive in practice. Instead, we develop a learning-based approach to synthesize realistic, diverse, and 3D pose-preserving hand images under the guidance of 3D pose information.

We propose a 3D-aware multi-modal guided hand generative network MM-Hand , together with a novel geometry-based curriculum learning strategy.

Our extensive experimental results demonstrate that the 3D-annotated images generated by MM-Hand qualitatively and quantitatively outperform existing options.

Moreover, the augmented data can consistently improve the quantitative performance of the state-of-the-art 3D hand pose estimators on two benchmark datasets.

In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions.

In this paper, we propose an effective algorithm, called JDNet, to solve the single image deraining problem and conduct the segmentation and detection task for applications. Specifically, considering the important information on multi-scale features, we propose a Scale-Aggregation module to learn the features with different scales. Simultaneously, Self-Attention module is introduced to match or outperform their convolutional counterparts, which allows the feature aggregation to adapt to each channel.

Furthermore, to improve the basic convolutional feature transformation process of Convolutional Neural Networks CNNs , Self-Calibrated convolution is applied to build long-range spatial and inter-channel dependencies around each spatial location that explicitly expand fields-of-view of each convolutional layer through internal communications and hence enriches the output features. By designing the Scale-Aggregation and Self-Attention modules with Self-Calibrated convolution skillfully, the proposed model has better deraining results both on real-world and synthetic datasets.

Extensive experiments are conducted to demonstrate the superiority of our method compared with state-of-the-art methods. The objective of action quality assessment is to score sports videos.

However, most existing works focus only on video dynamic information i. To learn more discriminative representations for videos, we not only learn the video dynamic information but also focus on the static postures of the detected athletes in specific frames, which represent the action quality at certain moments, along with the help of the proposed hybrid dynamic-static architecture. Moreover, we leverage a context-aware attention module consisting of a temporal instance-wise graph convolutional network unit and an attention unit for both streams to extract more robust stream features, where the former is for exploring the relations between instances and the latter for assigning a proper weight to each instance.

Finally, we combine the features of the two streams to regress the final video score, supervised by ground-truth scores given by experts. Additionally, we have collected and annotated the new Rhythmic Gymnastics dataset, which contains videos of four different types of gymnastics routines, for evaluation of action quality assessment in long videos. Extensive experimental results validate the efficacy of our proposed method, which outperforms related approaches.

In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and fast learning ability from limited data is necessarily required in real-world applications. Also, these existing methods are not well-suited for fast adaptation to a new category. Few-shot image generation, aiming to generate images from only a few images for a new category, has attracted some research interest.

In our F2GAN, a fusion generator is designed to fuse the high-level features of conditional images with random interpolation coefficients, and then fills in attended low-level details with non-local attention module to produce a new image.

Moreover, our discriminator can ensure the diversity of generated images by a mode seeking loss and an interpolation regression loss. Extensive experiments on five datasets demonstrate the effectiveness of our proposed method for few-shot image generation. We present a novel framework for human video motion transfer.

2021.5.24 CS papers

Using the baseline backend provided by the challenge organizers on the data enhanced with this multi-array front-end using the default parameters which differ slightly from the original paper a WER of In combination with an acoustic model presented by the RWTH Aachen [Kitza] this multi-array front-end achieved the third best results during the challenge with The best single system WERs with this enhancement are If you are using this code please cite the following paper pdf , poster :.

Speech_Recognition

Geometry-induced rich electronic properties in graphene nanoribbon are investigated by the first-principles calculations. Three types of graphene nanoribbons curved, bilayer and folding graphene nanoribbons are revealed to display the fundamental properties, such as optimal structures, ground state energies, magnetic moments, band structures, band gaps, band-edge states and density of states. Their electronic properties are dominated by the curvature effect, stacking effect, edge-edge interaction, and spin arrangements, etc. These properties can be modulated by the curvatures or the stacking configurations, and thus the metal-semiconductor transitions can be characterized. Specifically, for the zigzag systems, interesting features are displayed: the destruction or generation of magnetism and the splitting of spin-up and spin-down states. Versatile and intricate structures are exhibited in the density of states, including their forms, peak number, intensity and energy. Chapter 1.

MM '20: Proceedings of the 28th ACM International Conference on Multimedia - Part 2

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Accepted Papers: Main Conference

Creative rhythmic transformations of musical audio refer to automated methods for manipulation of temporally-relevant sounds in time. This paper presents a method for joint synthesis and rhythm transformation of drum sounds through the use of adversarial autoencoders AAE. Users may navigate both the timbre and rhythm of drum patterns in audio recordings through expressive control over a low-dimensional latent space. The model is based on an AAE with Gaussian mixture latent distributions that introduce rhythmic pattern conditioning to represent a wide variety of drum performances. The AAE is trained on a dataset of bar-length segments of percussion recordings, along with their clustered rhythmic pattern labels.

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Editorial Office of Design Engineering. Email: editor thedesignengineering. Journal print copy or article reprints are available for order, please contact: editor thedesignengineering. Permission For permission, please contact the editorial office directly: Email: editor thedesignengineering. Skip to main content Skip to main navigation menu Skip to site footer. Chellatamilan, N. Srinivasa Gupta, B. Valarmathi, K.

New Graduate Degree Program Proposal Information Form

Show all documents Results of the WNUT Shared Task on Novel and Emerging Entity Recognition With the novel and emerging entities recognition task , we aim to establish a new benchmark dataset and current state-of-the-art for the recognition of entities in the long tail. Most language expressions form a Zipfian distribution Zipf, ; Monte- murro, wherein a small number of very fre- quent observations occur and a very long tail of less frequent observations.

Information Retrieval Technology

RELATED VIDEO: Nikolaos Flemotomos - Linguistically Aided Speaker Diarization Using Speaker Role Information

Locked Full text available Biblio. Full text available Biblio. Of continuiteit? Een beschrijvende analyse van de burgemeestersconferenties in de provincie West-Vlaanderen en regio Rivierenland.

Python speaker-change-detection Libraries

Linearizability of eigenvector nonlinearities by Rob Claes et al. Mishra et al. Numerical differentiation on scattered data through multivariate polynomial interpolation by Francesco Dell'Accio et al. Partner Matters! Convergence analysis of the variational operator splitting scheme for a reaction-diffusion system with detailed balance by Chun Liu et al. IEEE Yes We Care!

This drone detection system uses YOLOv5 which is a family of object detection architectures and we have trained the model on Drone Dataset. Overview I. AutoHarness is a tool that automatically generates fuzzing harnesses for you. This idea stems from a concurrent problem in fuzzing codebases today: large codebases have thousands of functions and pieces of code that can be embedded fairly deep into the library.