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While log-amplitude mel-spectrogram has widely been used as the feature representation for processing speech based on deep learning, the effectiveness of another aspect of speech spectrum, i.e., phase information, was shown recently for tasks such as speech enhancement and source separation. In this study, we extensively investigated the effectiveness of including phase information of signals for eight audio classification tasks. We constructed a learnable front-end that can compute the phase and its derivatives based on a time-frequency representation with mel-like frequency axis.

Recently almost all the mainstream deepfake detection methods use Convolutional Neural Networks (CNN) as their backbone. However, due to the overreliance on local texture information, which is usually determined by forgery methods of training data, these CNN based methods cannot generalize well to unseen data. To get out of the predicament of prior methods, in this paper, we propose a novel transformer-based framework to model both global and local information and analyze anomalies of face images.

A method of interpolating the acoustic transfer function (ATF) between regions that takes into account both the physical properties of the ATF and the directionality of region configurations is proposed. Most spatial ATF interpolation methods are limited to estimation in the region of receivers. A kernel method for region-to-region ATF interpolation makes it possible to estimate the ATFs for both source and receiver regions from a discrete set of ATF measurements.

Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not necessarily correspond to large reconstruction errors. To address this issue, we design a Convolutional LSTM Auto-Encoder prediction framework with enhanced spatio-temporal memory exchange using bi-directionalilty and a higher-order mechanism. The bi-directional structure promotes learning the temporal regularity through forward and backward predictions.

Amyotrophic Lateral Sclerosis (ALS) is one of the most common neuromuscular diseases which affects both lower and upper motor neurons. In this paper, a dilated one dimensional convolutional neural network, named ALSNet, is proposed for identifying ALS from raw EMG signal. No hand-crafted feature extraction is required, rather, ALSNet is able to take raw EMG signal as input and detect EMG signals of ALS subjects. This makes the method more feasible for practical implementation by reducing the computational cost required for extracting features.

Violence detection is an essential and challenging problem in the computer vision community. Most existing works focus on single modal data analysis, which is not effective when multi-modality is available.

Amyotrophic Lateral Sclerosis (ALS) is one of the most common neuromuscular diseases which affects both lower and upper motor neurons. In this paper, a dilated one dimensional convolutional neural network, named ALSNet, is proposed for identifying ALS from raw EMG signal. No hand-crafted feature extraction is required, rather, ALSNet is able to take raw EMG signal as input and detect EMG signals of ALS subjects. This makes the method more feasible for practical implementation by reducing the computational cost required for extracting features.

Signal processing on directed graphs (digraphs) is problematic, since the graph shift, and thus associated filters, are in general not diagonalizable. Furthermore, the Fourier transform in this case is now obtained from the Jordan decomposition, which may not be computable at all for large graphs. We propose a novel and general solution for this problem based on matrix perturbation theory: We design an algorithm that adds a small number of edges to a given digraph to destroy nontrivial Jordan blocks.

Few-shot segmentation has got a lot of concerns recently. Existing methods mainly locate and recognize the target object based on a cross-guided way that applies masked target object features of sup- port(query) images to make a feature matching with query(support) images. However, there are some differences between support images and query images because of large appearance and scale variation, which will lead to inaccurate and incomplete segmentation. This problem inspired us to explore the local coherence of the image to guide the segmentation.