nils-herrmann/clean_scirepeval huggingface dataset:
Listen and Look: Audio–Visual Matching Assisted Speech Source Separation

Source permutation, i.e., assigning separated signal snippets to wrong sources over time, is a major issue in the state-of-the-art speaker-independent speech source separation methods. In addition to auditory cues, humans also leverage visual cues to solve this problem at cocktail parties: matching lip movements with voice fluctuations helps humans to better pay attention to the speaker of interest. In this letter, we propose an audio–visual matching network to learn the correspondence between voice fluctuations and lip movements. We then propose a framework to apply this network to address the source permutation problem and improve over audio-only speech separation methods. The modular design of this framework makes it easy to apply the matching network to any audio-only speech separation method. Experiments on two-talker mixtures show that the proposed approach significantly improves the separation quality over the state-of-the-art audio-only method. This improvement is especially pronounced on mixtures that the audio-only method fails, in which the speakers often have similar voice characteristics.
Blind Source Extraction Using Generalized Autocorrelations

This letter addresses blind (semiblind) source extraction (BSE) problem when a desired source signal has temporal structures, such as linear or nonlinear autocorrelations. Using the temporal characteristics of sources, we develop objective functions based on the generalized autocorrelations of primary sources. Maximizing the objective functions, we propose simple fixed-point source extraction algorithms. We give the stability analysis and prove convergence properties of the algorithms as the generalized autocorrelation function is linear or nonlinear. Especially, as the generalized autocorrelation function is linear, the algorithm has interesting character of "one-iteration" convergence under some conditions. Computer simulations and real-data application experiments show that the algorithms are appealing BSE methods for temporal signals of interest by capturing the linear or nonlinear autocorrelations of the desired sources.
Semi-Supervised Music Genre Classification

Music genre classification is a hot topic in pattern recognition and signal processing. Classical supervised methods need lost of labeled music data to train a classifier. In this paper, we propose a semi-supervised genre classification algorithm which is developed on several labeled music tracks and lots of unlabelled tracks. Three features are extracted from the each music track and manifold regularization method is used to design the classifier. Experiments on a large number of test music data show that semi-supervised method can improve the classification accuracy.
Dependent Online Kernel Learning With Constant Number of Random Fourier Features

Traditional online kernel learning analysis assumes independently identically distributed (i.i.d.) about the training sequence. Recent studies reveal that when the loss function is smooth and strongly convex, given T i.i.d. training instances, a constant sampling complexity of random Fourier features is sufficient to ensure O(log T/T) convergence rate of excess risk, which is optimal in online kernel learning up to a log T factor. However, the i.i.d. hypothesis is too strong in practice, which greatly impairs their value. In this paper, we study the sampling complexity of random Fourier features in online kernel learning under non-i.i.d. assumptions. We prove that the sampling complexity under non-i.i.d. settings is also constant, but the convergence rate of excess risk is O(log T/T+ φ ), where φ is the mixing coefficient measuring the extent of non-i.i.d. of training sequence. We conduct experiments both on artificial and real large-scale data sets to verify our theories.
Relevant linear feature extraction using side-information and unlabeled data

"Learning with side-information" is attracting more and more attention in machine learning problems. In this paper, we propose a general iterative framework for relevant linear feature extraction. It efficiently utilizes both the side-information and unlabeled data to enhance gradually algorithms' performance and robustness. Both good relevant feature extraction and reasonable similarity matrix estimation can be realized. Specifically, we adopt relevant component analysis (RCA) under this framework and get the derived iterative self-enhanced relevant component analysis (ISERCA) algorithm. The experimental results on several data sets show that ISERCA outperforms RCA.
Transductive object cutout

In this paper, we address the issue of transducing the object cutout model from an example image to novel image instances. We observe that although object and background are very likely to contain similar colors in natural images, it is much less probable that they share similar color configurations. Motivated by this observation, we propose a local color pattern model to characterize the color configuration in a robust way. Additionally, we propose an edge profile model to modulate the contrast of the image, which enhances edges along object boundaries and attenuates edges inside object or background. The local color pattern model and edge model are integrated in a graph-cut framework. Higher accuracy and improved robustness of the proposed method are demonstrated through experimental comparison with state-of-the-art algorithms.
A fast mean-field method for large-scale high-dimensional data and its application in colonic polyp detection at CT colonography

In this paper, we propose a fast mean-field method called LHMF to handle probabilistic models of large-scale data in high dimensional space. By using diffusion map locally linear embedding method which is a non-linear dimensionality reduction method, we first embed the high dimensional data into a low dimensional space. Then we construct a coarse-grained graph which preserves the spectral properties of original weighted graph in the high dimensional space by clustering. A new spin model is defined in the diffusion space and the geometric centroids of clusters represent variables in the new spin model. The calculation demand of mean-field methods can be reduced greatly on the coarse-grained spin model. The final marginal moments of original variables are derived from the states of geometric centroids by using geometric harmonics. We first tested the proposed method on the MNIST hand-written digits dataset. Experimental results show that the LHMF method is competent with consistency approach, a state-of-the-art semi-supervised learning method. Then we applied the proposed method to a large-scale colonic polyp dataset from computed tomography (CT) scans. Free-response operator characteristic analysis shows that our method achieves higher sensitivity with lower false positive rate compared with support vector machines.
Palmprint recognition using crease

The palmprint has one special salient feature which is not salient in fingerprint. That is the crease. In a palmprint the creases are large in number and comparatively easy to extract. Creases are also approximately stable in a person's whole life, which qualifies them as features in palmprint recognition. We give creases an accurate definition which is fit for algorithm implementation. We devised a rather exquisite algorithm to extract all the creases in a palmprint whose success is mainly from a new different direction computing method and a thorough local analysis and a robust search algorithm. Based on the extracted creases, we devised a robust palmprint matching algorithm which is rotation and translation invariant. The crease extraction results and palmprint matching results show that the crease can be extracted successfully and crease-based palmprint matching is robust and accurate.
Machine Learning Way for Boosting Accuracy in Canonical Correlation Analysis based Frequency Recognition

Canonical Correlation Analysis (CCA) is used to frequency recognition of multichannel signals. The unknown signals are compared against known templates and their frequencies are recognized by simply comparing the biggest coefficients of their CCA coefficient vectors. This strategy is straightforward but may not give optimal results. To boost the accuracy of recognition we reformulate the approach in views of machine learning. In this paper, we propose a new strategy based on supervised learning. We also employ feature selection within this framework to adopt efficient coefficients which may not be the largest coefficients for the features vectors. The recognition method is validated by results with real world data.
Dissimilarity reconstruction in information recommendation

A representation of objects in information recommendation named dissimilarity reconstruction (DSR) is proposed in this paper. DSR tries to simulate the gradually transferring mechanism in people's information evaluation process, capture the structure of a data set and retrieve its intrinsic dimensionality. Dissimilarities between objects are first obtained from Vector Space Model (VSM) and then a low-dimensional space is reconstructed by the nonlinear technique Isomap. In the space, Euclidean distance between the associated vectors of two arbitrary objects well represents the dissimilarity between them in sense of evaluation. Experiment on a data set of user activities at bulletin board systems (BBS) has demonstrated the rationality of this representation.
Interactive Natural Image Segmentation via Spline Regression

This paper presents an interactive algorithm for segmentation of natural images. The task is formulated as a problem of spline regression, in which the spline is derived in Sobolev space and has a form of a combination of linear and Green's functions. Besides its nonlinear representation capability, one advantage of this spline in usage is that, once it has been constructed, no parameters need to be tuned to data. We define this spline on the user specified foreground and background pixels, and solve its parameters (the combination coefficients of functions) from a group of linear equations. To speed up spline construction, K-means clustering algorithm is employed to cluster the user specified pixels. By taking the cluster centers as representatives, this spline can be easily constructed. The foreground object is finally cut out from its background via spline interpolation. The computational complexity of the proposed algorithm is linear in the number of the pixels to be segmented. Experiments on diverse natural images, with comparison to existing algorithms, illustrate the validity of our method.
Unsupervised Single-Channel Music Source Separation by Average Harmonic Structure Modeling

Source separation of musical signals is an appealing but difficult problem, especially in the single-channel case. In this paper, an unsupervised single-channel music source separation algorithm based on average harmonic structure modeling is proposed. Under the assumption of playing in narrow pitch ranges, different harmonic instrumental sources in a piece of music often have different but stable harmonic structures; thus, sources can be characterized uniquely by harmonic structure models. Given the number of instrumental sources, the proposed algorithm learns these models directly from the mixed signal by clustering the harmonic structures extracted from different frames. The corresponding sources are then extracted from the mixed signal using the models. Experiments on several mixed signals, including synthesized instrumental sources, real instrumental sources, and singing voices, show that this algorithm outperforms the general nonnegative matrix factorization (NMF)-based source separation algorithm, and yields good subjective listening quality. As a side effect, this algorithm estimates the pitches of the harmonic instrumental sources. The number of concurrent sounds in each frame is also computed, which is a difficult task for general multipitch estimation (MPE) algorithms.
Maximum Margin Clustering with Multivariate Loss Function

This paper presents a simple but powerful extension of the maximum margin clustering (MMC) algorithm that optimizes multivariate performance measure specifically defined for clustering, including Normalized Mutual In- formation, Rand Index and F-measure. Different from previous MMC algorithms that always employ the error rate as the loss function, our formulation involves a multivariate loss function that is a non-linear combination of the individual clustering results. Computationally, we propose a cutting plane algorithm to approximately solve the resulting optimization problem with a guaranteed accuracy. Experimental evaluations show clear improvements in clustering performance of our method over previous maximum margin clustering algorithms.
Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks

Traffic sign recognition (TSR) is an important and challenging task for intelligent transportation systems. We describe the details of our model's architecture for TSR and suggest a hinge loss stochastic gradient descent (HLSGD) method to train convolutional neural networks (CNNs). Our CNN consists of three stages (70-110-180) with 1162 284 trainable parameters. The HLSGD is evaluated on the German Traffic Sign Recognition Benchmark, which offers a faster and more stable convergence and a state-of-the-art recognition rate of 99.65%. We write a graphics processing unit package to train several CNNs and establish the final classifier in an ensemble way.
Spectral feature analysis

We have seen a surge of interest in spectral-based methods and kernel-based methods for machine learning and data mining. Despite the significant research, these methods remain only loosely related. In this paper, we give theoretically an explicit relation between spectral clustering and weighted kernel principal component analysis (WKPCA). We show that spectral clustering is not only a method for data clustering, but also for feature extraction. We are then able to reinterpret the spectral clustering algorithm in terms of WKPCA and propose our spectral feature analysis (SFA) method. The spectral features extracted by SFA can capture the distinguishing information of data from different classes effectively. Finally some experimental results are presented to show the effectiveness of our method.
Real-Time Traffic Light Detection With Adaptive Background Suppression Filter

Traffic light detection plays an important role in intelligent transportation system, and many detection methods have been proposed in recent years. However, illumination variation effect is still of its major technical problem in real urban driving environments. In this paper, we propose a novel vision-based traffic light detection method for driving vehicles, which is fast and robust under different illumination conditions. The proposed method contains two stages: the candidate extraction stage and the recognition stage. On the candidate extraction stage, we propose an adaptive background suppression algorithm to highlight the traffic light candidate regions while suppressing the undesired backgrounds. On the recognition stage, each candidate region is verified and is further classified into different traffic light semantic classes. We evaluate our method on video sequences (more than 5000 frames and labels) captured from urban streets and suburb roads in varying illumination and compared with other vision-based traffic detection approaches. The experiment shows that the proposed method can achieve a desired detection result with high quality and robustness; simultaneously, the whole detection system can meet the real-time processing requirement of about 15 fps on video sequences.
Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts

Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image captioning system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifts among the visual regions—such transitions impose a thread of ordering in visual perception. This alignment characterizes the flow of latent meaning, which encodes what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets, using both automatic evaluation metrics and human evaluation. We show that either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance.
Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method
Automatic Elements Extraction of Chinese Web News Using Prior Information of Content and Structure

We propose a set of efficient processes for extracting all four elements of Chinese news web pages, namely news title, release date, news source and the main text. Our approach is based on a deep analysis of content and structure features of current Chinese news. We take content indicators as the key to recover tree structure of the main text. Additionally, we come up with the concept of Length-Distance Ratio to help improve performance. Our method rarely depends on selection of samples and has strong generalization ability regardless of training process, distinguishing itself from most existing methods. We have tested our approach on 1721 labeled Chinese news pages from 429 web sites. Results show that an 87% accuracy was achieved for news source extraction, and over 95% accuracy for other three elements.
Blindly separating mixtures of multiple layers with spatial shifts

We address the problem of blindly separating mixtures of multiple layer images with unknown spatial shifts and mixing coefficients. Our proposed method can handle the over-determined, determined and under-determined cases where mixtures are more than, as many as and fewer than layers, respectively. The method is fast in over-determined and determined cases, with the same complexity as the fast Fourier transform (FFT), and can separate more layers from fewer mixtures in the under-determined case. It consists of two main steps. First, a novel sparse blind separation algorithm is applied, to estimate the spatial shifts, the mixing coefficients and the edge image of each layer. Second, all layers are reconstructed, by large scale linear programming in the under-determined case, or by least-squares solutions in other cases. The effectiveness of this technology is shown in the experiments on two simulated mixtures of four layers with spatial shifts, real mixture photos containing transparency and reflections, and real mixture images in a dissolve from a video.
Learning distance metric for semi-supervised image segmentation

Semi-supervised image segmentation is an important issue in many image processing applications, and has been a popular research area recently, the most popular are graph-based methods. However, parameter selection in these methods is still largely heuristic. In this paper, we introduce distance metric learning into graph-based semi-supervised segmentation to automatically obtain good results for images with different appearances. We first derive the optimization problem with respect to the distance metric as well as the segmentation labels, and use gradient descent method to find a local optimum solution. Experiments on general images and the fungal disease analysis application have shown that our method provides a steady performance under casual user annotations and different image appearances.
A Fixed-Point Algorithm for Blind Separation of Temporally Correlated Sources

In this paper we develop a new method for blind separation of temporally correlated sources, possibly dependent signals from linear mixtures of them. The proposed algorithm is based on the mutual independency of the innovations of source signals instead of original signals. This algorithm takes into account both the temporal structure and the high-order statistics of source signals and in contrast to the most known blind separation algorithms only exploiting the second order statistics or the non-Gaussianity. In this framework, a fixed-point algorithm is introduced. The fixed-point algorithm is computationally very simple, converge fast, and does not need choose any learning step sizes. Extensive computer simulations with speech signals and images confirm the validity and high performance of the proposed algorithm.
Learning Efficient Convolutional Networks through Network Slimming

The deployment of deep convolutional neural networks (CNNs) in many real world applications is largely hindered by their high computational cost. In this paper, we propose a novel learning scheme for CNNs to simultaneously 1) reduce the model size; 2) decrease the run-time memory footprint; and 3) lower the number of computing operations, without compromising accuracy. This is achieved by enforcing channel-level sparsity in the network in a simple but effective way. Different from many existing approaches, the proposed method directly applies to modern CNN architectures, introduces minimum overhead to the training process, and requires no special software/hardware accelerators for the resulting models. We call our approach network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy. We empirically demonstrate the effectiveness of our approach with several state-of-the-art CNN models, including VGGNet, ResNet and DenseNet, on various image classification datasets. For VGGNet, a multi-pass version of network slimming gives a 20× reduction in model size and a 5× reduction in computing operations.
Metric learning based data augmentation for environmental sound classification

Deep neural networks have been widely applied in the field of environmental sound classification. However, due to the scarcity of carefully labeled data, their training process suffers from over-fitting. Data augmentation is a technique that alleviates this issue. It augments the training set with synthetic data that are created by modifying some parameters of the real data. However, not all kinds of augmentations are helpful, and some are in fact harmful for the recognition of certain sound concepts. Figuring out the appropriate augmentations for the appropriate training data is thus an interesting question. In this paper, we propose a framework for data augmentation through metric learning. The idea is to first learn a metric from the original training data, and then use it to filter out augmented data samples that are far from original ones in the same class. Experiments on a widely used dataset show that our framework achieves the same performance compared to other augmentation strategies while reducing the amount of training data by a large margin.