Automatic Detection and Compression for Passive Acoustic Monitoring of the African Forest Elephant

In this work, we consider applying machine learning to the analysis and compression of audio signals in the context of monitoring elephants in sub-Saharan Africa. Earth's biodiversity is increasingly under threat by sources of anthropogenic change (e.g. resource extraction, land use change, and climate change) and surveying animal populations is critical for developing conservation strategies. However, manually monitoring tropical forests or deep oceans is intractable. For species that communicate acoustically, researchers have argued for placing audio recorders in the habitats as a cost-effective and non-invasive method, a strategy known as passive acoustic monitoring (PAM). In collaboration with conservation efforts, we construct a large labeled dataset of passive acoustic recordings of the African Forest Elephant via crowdsourcing, compromising thousands of hours of recordings in the wild. Using state-of-the-art techniques in artificial intelligence we improve upon previously proposed methods for passive acoustic monitoring for classification and segmentation. In real-time detection of elephant calls, network bandwidth quickly becomes a bottleneck and efficient ways to compress the data are needed. Most audio compression schemes are aimed at human listeners and are unsuitable for low-frequency elephant calls. To remedy this, we provide a novel end-to-end differentiable method for compression of audio signals that can be adapted to acoustic monitoring of any species and dramatically improves over naive coding strategies

Facial Expression Analysis under Partial Occlusion: A Survey

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment and human computer interaction. The vast majority of completed FEA studies are based on non-occluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work.Comment: Authors pre-print of the article accepted for publication in ACM Computing Surveys (accepted on 02-Nov-2017

Multimodal Sensing and Data Processing for Speaker and Emotion Recognition using Deep Learning Models with Audio, Video and Biomedical Sensors

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection. The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker recognition or as a substitute in noisy or poorly-lit environments. Secondly, the effectiveness of deep learning is empirically confirmed through its higher robustness to all types of features in comparison to a number of commonly used baseline classifiers. Not only do deep models outperform the baseline methods, their power increases when they integrate multiple modalities, as different modalities contain information on different aspects of the data, especially between EMG and audio. Interestingly, our deep learning approach is word-independent. Plus, the EMG, audio, and visual parts of the samples from each speaker do not need to match. This increases the flexibility of our method in using multimodal data, particularly if one or more modalities are missing. With a dataset of 23 individuals speaking 22 words five times, we show that EMG can replace the audio/visual modalities, and when combined, significantly improve the accuracy of speaker recognition. The second part describes a study on automated emotion recognition using four different modalities – audio, video, electromyography (EMG), and electroencephalography (EEG). We collected a dataset by recording the 4 modalities as 12 human subjects expressed six different emotions or maintained a neutral expression. Three different aspects of emotion recognition were investigated: model selection, feature selection, and data selection. Both generative models (DBNs) and discriminative models (LSTMs) were applied to the four modalities, and from these analyses we conclude that LSTM is better for audio and video together with their corresponding sophisticated feature extractors (MFCC and CNN), whereas DBN is better for both EMG and EEG. By examining these signals at different stages (pre-speech, during-speech, and post-speech) of the current and following trials, we have found that the most effective stages for emotion recognition from EEG occur after the emotion has been expressed, suggesting that the neural signals conveying an emotion are long-lasting

Feature Extraction

Feature extraction is a procedure aimed at selecting and transforming a data set in order to increase the performance of a pattern recognition or machine learning system. Nowadays, since the amount of data available and its dimension is growing exponentially, it is a fundamental procedure to avoid overfitting and the curse of dimensionality, while, in some cases, allowing a interpretative analysis of the data. The topic itself is a thriving discipline of study, and it is difficult to address every single feature extraction algorithm. Therefore, we provide an overview of the topic, introducing widely used techniques, while at the same time presenting some domain-specific feature extraction algorithms. Finally, as a case, study, we will illustrate the vastness of the field by analysing the usage and impact of feature extraction in neuroimaging

A survey on artificial intelligence-based acoustic source identification

The concept of Acoustic Source Identification (ASI), which refers to the process of identifying noise sources has attracted increasing attention in recent years. The ASI technology can be used for surveillance, monitoring, and maintenance applications in a wide range of sectors, such as defence, manufacturing, healthcare, and agriculture. Acoustic signature analysis and pattern recognition remain the core technologies for noise source identification. Manual identification of acoustic signatures, however, has become increasingly challenging as dataset sizes grow. As a result, the use of Artificial Intelligence (AI) techniques for identifying noise sources has become increasingly relevant and useful. In this paper, we provide a comprehensive review of AI-based acoustic source identification techniques. We analyze the strengths and weaknesses of AI-based ASI processes and associated methods proposed by researchers in the literature. Additionally, we did a detailed survey of ASI applications in machinery, underwater applications, environment/event source recognition, healthcare, and other fields. We also highlight relevant research directions

Random Regression Forests for Acoustic Event Detection and Classification

Despite the success of the automatic speech recognition framework in its own application field, its adaptation to the problem of acoustic event detection has resulted in limited success. In this paper, instead of treating the problem similar to the segmentation and classification tasks in speech recognition, we pose it as a regression task and propose an approach based on random forest regression. Furthermore, event localization in time can be efficiently handled as a joint problem. We first decompose the training audio signals into multiple interleaved superframes which are annotated with the corresponding event class labels and their displacements to the temporal onsets and offsets of the events. For a specific event category, a random-forest regression model is learned using the displacement information. Given an unseen superframe, the learned regressor will output the continuous estimates of the onset and offset locations of the events. To deal with multiple event categories, prior to the category-specific regression phase, a superframe-wise recognition phase is performed to reject the background superframes and to classify the event superframes into different event categories. While jointly posing event detection and localization as a regression problem is novel, the superior performance on two databases ITC-Irst and UPC-TALP demonstrates the efficiency and potential of the proposed approach

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Individual and Inter-related Action Unit Detection in Videos for Affect Recognition

The human face has evolved to become the most important source of non-verbal information that conveys our affective, cognitive and mental state to others. Apart from human to human communication facial expressions have also become an indispensable component of human-machine interaction (HMI). Systems capable of understanding how users feel allow for a wide variety of applications in medical, learning, entertainment and marketing technologies in addition to advancements in neuroscience and psychology research and many others. The Facial Action Coding System (FACS) has been built to objectively define and quantify every possible facial movement through what is called Action Units (AU), each representing an individual facial action. In this thesis we focus on the automatic detection and exploitation of these AUs using novel appearance representation techniques as well as incorporation of the prior co-occurrence information between them. Our contributions can be grouped in three parts. In the first part, we propose to improve the detection accuracy of appearance features based on local binary patterns (LBP) for AU detection in videos. For this purpose, we propose two novel methodologies. The first one uses three fundamental image processing tools as a pre-processing step prior to the application of the LBP transform on the facial texture. These tools each enhance the descriptive ability of LBP by emphasizing different transient appearance characteristics, and are proven to increase the AU detection accuracy significantly in our experiments. The second one uses multiple local curvature Gabor binary patterns (LCGBP) for the same problem and achieves state-of-the-art performance on a dataset of mostly posed facial expressions. The curvature information of the face, as well as the proposed multiple filter size scheme is very effective in recognizing these individual facial actions. In the second part, we propose to take advantage of the co-occurrence relation between the AUs, that we can learn through training examples. We use this information in a multi-label discriminant Laplacian embedding (DLE) scheme to train our system with SIFT features extracted around the salient and transient landmarks on the face. The system is first validated on a challenging (containing lots of occlusions and head pose variations) dataset without the DLE, then we show the performance of the full system on the FERA 2015 challenge on AU occurence detection. The challenge consists of two difficult datasets that contain spontaneous facial actions at different intensities. We demonstrate that our proposed system achieves the best results on these datasets for detecting AUs. The third and last part of the thesis contains an application on how this automatic AU detection system can be used in real-life situations, particularly for detecting cognitive distraction. Our contribution in this part is two-fold: First, we present a novel visual database of people driving a simulator while being induced visual and cognitive distraction via secondary tasks. The subjects have been recorded using three near-infrared camera-lighting systems, which makes it a very suitable configuration to use in real driving conditions, i.e. with large head pose and ambient light variations. Secondly, we propose an original framework to automatically discriminate cognitive distraction sequences from baseline sequences by extracting features from continuous AU signals and by exploiting the cross-correlations between them. We achieve a very high classification accuracy in our subject-based experiments and a lower yet acceptable performance for the subject-independent tests. Based on these results we discuss how facial expressions related to this complex mental state are individual, rather than universal, and also how the proposed system can be used in a vehicle to help decrease human error in traffic accidents