Adaptive DCTNet for Audio Signal Classification

In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.Comment: International Conference of Acoustic and Speech Signal Processing (ICASSP). New Orleans, United States, March, 201

PLDANet: Reasonable Combination of PCA and LDA Convolutional Networks

Integrating deep learning with traditional machine learning methods is an intriguing research direction. For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately. It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. Therefore, it is meaningful to adopt LDA to learn filter kernels only when all the patches carry information from the whole image. However, to our knowledge, there are no existing works that study how to combine PCA and LDA to learn convolutional kernels to achieve the best performance. In this paper, we propose the convolutional coverage theory. Furthermore, we propose the PLDANet model which adopts PCA and LDA reasonably in different convolutional layers based on the coverage theory. The experimental study has shown the effectiveness of the proposed PLDANet model

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection

Nowadays, the care of the elderly has become a social concern. The fall of the elderly has become one of the main factors threatening the health of the elderly. In this paper, we designed a fall detection algorithm based on human joint extraction and object detection.First,yolov4 was used to identify and detect the elderly. Then openpose was used to detect the human joint. Based on the human joint, this paper using Random Forest to classify the status of the elderly, there are three states of the elderly: falling down, lying down and other states. In the detection of a single old man, the accuracy of the model reached 99.3%, the sensitivity and specificity of the model reached 79.3% and 72.1%