Non-Negative Discriminative Data Analytics

Due to advancements in data acquisition techniques, collecting datasets representing samples from multi-views has become more common recently (Jia et al. 2019). For instance, in genomics, a lymphoma patient’s dataset may include data on gene expression, single nucleotide polymorphism (SNP), and array Comparative genomic hybridization (aCGH) measurements. Learning from multiple views about the same objective, in general, obtains a better understanding of the hidden patterns of the data compared to learning from a single view data. Most of the existing multi-view learning techniques such as canonical correlation analysis (Hotelling et al. 1936) and multi-view support vector machine (Farquhar et al. 2006), multiple kernel learning (Zhang et al. 2016) are focused on extracting the shared information among multiple datasets. However, in some real-world applications, it’s appealing to extract the discriminative knowledge of multiple datasets, namely discriminative data analytics. For example, consider the one dataset as gene-expression measurements of cancer patients, and the other dataset as the gene-expression levels of healthy volunteers and the goal is to cluster cancer patients according to the molecular sub-types. Performing a single view analysis such as principal component analysis (PCA) on any of the dataset yields information related to the common knowledge between the two datasets (Garte et al. 1996). Addressing such challenge, contrastive PCA (Abid et al. 2017) and discriminative (d) PCA in (Jia et al. 2019) are proposed in to extract one dataset-specific information often missed by PCA. Inspired by dPCA, we propose a novel discriminative multi-view learning algorithm, namely Non-negative Discriminative Analysis (DNA), to extract the unique information of one dataset (a.k.a. view) with respect to the other dataset. This boils down to solving a non-negative matrix factorization problem. Furthermore, we apply the proposed DNA framework in various real-world down-stream machine learning applications such as feature selections, dimensionality reduction, classification, and clustering

DNN Transfer Learning based Non-linear Feature Extraction for Acoustic Event Classification

Recent acoustic event classification research has focused on training suitable filters to represent acoustic events. However, due to limited availability of target event databases and linearity of conventional filters, there is still room for improving performance. By exploiting the non-linear modeling of deep neural networks (DNNs) and their ability to learn beyond pre-trained environments, this letter proposes a DNN-based feature extraction scheme for the classification of acoustic events. The effectiveness and robustness to noise of the proposed method are demonstrated using a database of indoor surveillance environments

Heart Rate Estimation from Phonocardiogram Signals Using Non-negative Matrix Factorization

International audienceElectrocardiogram (ECG) is classically considered for heart rate (HR) estimation. However in certain conditions, its use may be difficult and alternative techniques, such as phonocardiograhpy (PCG), are investigated. For PCG signals, in most studies, the challenge is to detect and annotate the heart sounds S 1 and S 2 , which may become quasi-impossible in case of noise. In this paper, we present a novel approach of HR estimation from PCG signals based on non-negative matrix factorization (NMF), applied to the spectrogram of PCG, considered as a source-filter model. Compared to state of the art methods, specific considerations based on the signal properties have been included to ensure the reliability of the decomposition. HR estimations obtained from noise-free and noisy real PCG signals are evaluated by comparison to HR estimation from synchronous ECG

Can Machine Learning Be Used to Recognize and Diagnose Coughs?

Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.Comment: Accepted in IEEE International Conference on E-Health and Bioengineering - EHB 202

Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI-powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.Comment: 30 pages, 12 figures, 9 table

Characterizing Audio Events for Video Soundtrack Analysis

There is an entire emerging ecosystem of amateur video recordings on the internet today, in addition to the abundance of more professionally produced content. The ability to automatically scan and evaluate the content of these recordings would be very useful for search and indexing, especially as amateur content tends to be more poorly labeled and tagged than professional content. Although the visual content is often considered to be of primary importance, the audio modality contains rich information which may be very helpful in the context of video search and understanding. Any technology that could help to interpret video soundtrack data would also be applicable in a number of other scenarios, such as mobile device audio awareness, surveillance, and robotics. In this thesis we approach the problem of extracting information from these kinds of unconstrained audio recordings. Specifically we focus on techniques for characterizing discrete audio events within the soundtrack (e.g. a dog bark or door slam), since we expect events to be particularly informative about content. Our task is made more complicated by the extremely variable recording quality and noise present in this type of audio. Initially we explore the idea of using the matching pursuit algorithm to decompose and isolate components of audio events. Using these components we develop an approach for non-exact (approximate) fingerprinting as a way to search audio data for similar recurring events. We demonstrate a proof of concept for this idea. Subsequently we extend the use of matching pursuit to build an actual audio fingerprinting system, with the goal of identifying simultaneously recorded amateur videos (i.e. videos taken in the same place at the same time by different people, which contain overlapping audio). Automatic discovery of these simultaneous recordings is one particularly interesting facet of general video indexing. We evaluate this fingerprinting system on a database of 733 internet videos. Next we return to searching for features to directly characterize soundtrack events. We develop a system to detect transient sounds and represent audio clips as a histogram of the transients it contains. We use this representation for video classification over a database of 1873 internet videos. When we combine these features with a spectral feature baseline system we achieve a relative improvement of 7.5% in mean average precision over the baseline. In another attempt to devise features to better describe and compare events, we investigate decomposing audio using a convolutional form of non-negative matrix factorization, resulting in event-like spectro-temporal patches. We use the resulting representation to build an event detection system that is more robust to additive noise than a comparative baseline system. Lastly we investigate a promising feature representation that has been used by others previously to describe event-like sound effect clips. These features derive from an auditory model and are meant to capture fine time structure in sound events. We compare these features and a related but simpler feature set on the task of video classification over 9317 internet videos. We find that combinations of these features with baseline spectral features produce a significant improvement in mean average precision over the baseline