Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness

Audio Deepfake Detection: A Survey

Audio deepfake detection is an emerging active topic. A growing number of literatures have aimed to study deepfake detection algorithms and achieved effective performance, the problem of which is far from being solved. Although there are some review literatures, there has been no comprehensive survey that provides researchers with a systematic overview of these developments with a unified evaluation. Accordingly, in this survey paper, we first highlight the key differences across various types of deepfake audio, then outline and analyse competitions, datasets, features, classifications, and evaluation of state-of-the-art approaches. For each aspect, the basic techniques, advanced developments and major challenges are discussed. In addition, we perform a unified comparison of representative features and classifiers on ASVspoof 2021, ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively. The survey shows that future research should address the lack of large scale datasets in the wild, poor generalization of existing detection methods to unknown fake attacks, as well as interpretability of detection results

Advances in Binary and Multiclass Audio Segmentation with Deep Learning Techniques

Los avances tecnológicos acaecidos en la última década han cambiado completamente la forma en la que la población interactúa con el contenido multimedia. Esto ha propiciado un aumento significativo tanto en la generación como el consumo de dicho contenido. El análisis y la anotación manual de toda esta información no son factibles dado el gran volumen actual, lo que releva la necesidad de herramientas automáticas que ayuden en la transición hacia flujos de trabajo asistidos o parcialmente automáticos. En los últimos años, la mayoría de estas herramientas están basadas en el uso de redes neuronales y deep learning. En este contexto, el trabajo que se describe en esta tesis se centra en el ámbito de la extracción de información a partir de señales de audio. Particularmente, se estudia la tarea de segmentación de audio, cuyo principal objetivo es obtener una secuencia de etiquetas que aíslen diferentes regiones en una señal de entrada de acuerdo con una serie de características descritas en un conjunto predefinido de clases, como por ejemplo voz, música o ruido.La primera parte de esta memoria esta centrada en la tarea de detección de actividad de voz. Recientemente, diferentes campañas de evaluación internacionales han propuesto esta tarea como uno de sus retos. Entre ellas se encuentra el reto Fearless steps, que trabaja con audios de las grabaciones de las misiones Apollo de la NASA. Para este reto, se propone una solución basada en aprendizaje supervisado usando una red convolucional recurrente como clasificador. La principal contribución es un método que combina información de filtros de 1D y 2D en la etapa convolucional para que sea procesada posteriormente por la etapa recurrente. Motivado por la introducción de los datos del reto Fearless steps, se plantea una evaluación de diferentes técnicas de adaptación de dominio, con el objetivo de comprobar las prestaciones de un sistema entrenado con datos de dominios habituales y evaluado en este nuevo dominio presentado en el reto. Los métodos descritos no requieren de etiquetas en el dominio objetivo, lo que facilita su uso en aplicaciones prácticas. En términos generales, se observa que los métodos que buscan minimizar el cambio en las distribuciones estadísticas entre los dominios fuente y objetivo obtienen los resultados mas prometedores. Los avances recientes en técnicas de representación obtenidas mediante aprendizaje auto-supervisado han demostrado grandes mejoras en prestaciones en varias tareas relacionadas con el procesado de voz. Siguiendo esta línea, se plantea la incorporación de dichas representaciones en la tarea de detección de actividad de voz. Las ediciones más recientes del reto Fearless steps modificaron su propósito, buscando ahora evaluar las capacidades de generalización de los sistemas. El objetivo entonces con las técnicas introducidas es poder beneficiarse de grandes cantidades de datos no etiquetados para mejorar la robustez del sistema. Los resultados experimentales sugieren que el aprendizaje auto-supervisado de representaciones permite obtener sistemas que son mucho menos sensibles al cambio de dominio.En la segunda parte de este documento se analiza una tarea de segmentación de audio más genérica que busca clasificar de manera simultanea una señal de audio como voz, música, ruido o una combinación de estas. En el contexto de los datos propuesto para el reto de segmentación de audio Albayzín 2010, se presenta un enfoque basado en el uso de redes neuronales recurrentes como clasificador principal, y un modelo de postprocesado integrado por modelos ocultos de Markov. Se introduce un nuevo bloque en la arquitectura neuronal con el objetivo de eliminar la información temporal redundante, mejorando las prestaciones y reduciendo el numero de operaciones por segundo al mismo tiempo. Esta propuesta obtuvo mejores prestaciones que soluciones presentadas anteriormenteen la literatura, y que aproximaciones similares basadas en redes neuronales profundas. Mientras que los resultados con aprendizaje auto-supervisado de representaciones eran prometedores en tareas de segmentación binaria, si se aplican en tareas de segmentación multiclase surgen una serie de cuestiones. Las técnicas habituales de aumento de datos que se aplican en el entrenamiento fuerzan al modelo a compensar el ruido de fondo o la música. En estas condiciones las características obtenidas podrían no representar de manera precisa aquellas clases generadas de manera similar a las versiones aumentadas vistas en el entrenamiento. Este hecho limita la mejora global de prestaciones observada al aplicar estas técnicas en tareas como la propuesta en la evaluación Albayzín 2010.La última parte de este trabajo ha investigado la aplicación de nuevas funciones de coste en la tarea de segmentación de audio, con el principal objetivo de mitigar los problemas que se derivan de utilizar un conjunto de datos de entrenamiento limitado. Se ha demostrado que nuevas técnicas de optimización basadas en las métricas AUC y AUC parcial pueden mejorar objetivos de entrenamiento tradicionales como la entropía cruzada en varias tareas de detección. Con esta idea en mente, en esta tesis se introducen dichas técnicas en la tarea de detección de música. Considerando que la cantidad de datos etiquetados para esta tarea es limitada comparado con otras tareas, las funciones de coste basadas en la métrica AUC se aplican con el objetivo de mejorar las prestaciones cuando el conjunto de datos de entrenamiento es relativamente pequeño. La mayoría de los sistemas que utilizan las técnicas de optimización basadas en métricas AUC se limitan a tareas binarias ya que ese el ámbito de aplicación habitual de la métrica AUC. Además, el etiquetado de audios con taxonomías más detalladas en las que hay múltiples opciones posibles es más complejo, por lo que la cantidad de audio etiquetada en algunas tareas de segmentación multiclase es limitada. Como una extensión natural, se propone una generalización de las técnicas de optimización basadas en la métrica AUC binaria, de tal manera que se puedan aplicar con un número arbitrario de clases. Dos funciones de coste distintas se introducen, usando como base para su formulación las variaciones multiclase de la métrica AUC propuestas en la literatura: una basada en un enfoque uno contra uno, y otra basada en un enfoque uno contra el resto.<br /

Novel perspectives and approaches to video summarization

The increasing volume of videos requires efficient and effective techniques to index and structure videos. Video summarization is such a technique that extracts the essential information from a video, so that tasks such as comprehension by users and video content analysis can be conducted more effectively and efficiently. The research presented in this thesis investigates three novel perspectives of the video summarization problem and provides approaches to such perspectives. Our first perspective is to employ local keypoint to perform keyframe selection. Two criteria, namely Coverage and Redundancy, are introduced to guide the keyframe selection process in order to identify those representing maximum video content and sharing minimum redundancy. To efficiently deal with long videos, a top-down strategy is proposed, which splits the summarization problem to two sub-problems: scene identification and scene summarization. Our second perspective is to formulate the task of video summarization to the problem of sparse dictionary reconstruction. Our method utilizes the true sparse constraint L0 norm, instead of the relaxed constraint L2,1 norm, such that keyframes are directly selected as a sparse dictionary that can reconstruct the video frames. In addition, a Percentage Of Reconstruction (POR) criterion is proposed to intuitively guide users in selecting an appropriate length of the summary. In addition, an L2,0 constrained sparse dictionary selection model is also proposed to further verify the effectiveness of sparse dictionary reconstruction for video summarization. Lastly, we further investigate the multi-modal perspective of multimedia content summarization and enrichment. There are abundant images and videos on the Web, so it is highly desirable to effectively organize such resources for textual content enrichment. With the support of web scale images, our proposed system, namely StoryImaging, is capable of enriching arbitrary textual stories with visual content

Development of algorithms for smart hearing protection devices

In industrial environments, wearing hearing protection devices is required to protect the wearers from high noise levels and prevent hearing loss. In addition to their protection against excessive noise, hearing protectors block other types of signals, even if they are useful and convenient. Therefore, if people want to communicate and exchange information, they must remove their hearing protectors, which is not convenient, or even dangerous. To overcome the problems encountered with the traditional passive hearing protection devices, this thesis outlines the steps and the process followed for the development of signal processing algorithms for a hearing protector that allows protection against external noise and oral communication between wearers. This hearing protector is called the “smart hearing protection device”. The smart hearing protection device is a traditional hearing protector in which a miniature digital signal processor is embedded in order to process the incoming signals, in addition to a miniature microphone to pickup external signals and a miniature internal loudspeaker to transmit the processed signals to the protected ear. To enable oral communication without removing the smart hearing protectors, signal processing algorithms must be developed. Therefore, the objective of this thesis consists of developing a noise-robust voice activity detection algorithm and a noise reduction algorithm to improve the quality and intelligibility of the speech signal. The methodology followed for the development of the algorithms is divided into three steps: first, the speech detection and noise reduction algorithms must be developed, second, these algorithms need to be evaluated and validated in software, and third, they must be implemented in the digital signal processor to validate their feasibility for the intended application. During the development of the two algorithms, the following constraints must be taken into account: the hardware resources of the digital signal processor embedded in the hearing protector (memory, number of operations per second), and the real-time constraint since the algorithm processing time should not exceed a certain threshold not to generate a perceptible delay between the active and passive paths of the hearing protector or a delay between the lips movement and the speech perception. From a scientific perspective, the thesis determines the thresholds that the digital signal processor should not exceed to not generate a perceptible delay between the active and passive paths of the hearing protector. These thresholds were obtained from a subjective study, where it was found that this delay depends on different parameters: (a) the degree of attenuation of the hearing protector, (b) the duration of the signal, (c) the level of the background noise, and (d) the type of the background noise. This study showed that when the fit of the hearing protector is shallow, 20 % of participants begin to perceive a delay after 8 ms for a bell sound (transient), 16 ms for a clean speech signal and 22 ms for a speech signal corrupted by babble noise. On the other hand, when having a deep hearing rotection fit, it was found that the delay between the two paths is 18 ms for the bell signal, 26 ms for the speech signal without noise and no delay when speech is corrupted by babble noise, showing that a better attenuation allows more time for digital signal processing. Second, this work presents a new voice activity detection algorithm in which a low complexity speech characteristic has been extracted. This characteristic was calculated as the ratio between the signal’s energy in the frequency region that contains the first formant to characterize the speech signal, and the low or high frequencies to characterize the noise signals. The evaluation of this algorithm and its comparison to another benchmark algorithm has demonstrated its selectivity with a false positive rate averaged over three signal to noise ratios (SNR) (10, 5 and 0 dB) of 4.2 % and a true positive rate of 91.4 % compared to 29.9 % false positives and 79.0 % of true positives for the benchmark algorithm. Third, this work shows that the extraction of the temporal envelope of a signal to generate a nonlinear and adaptive gain function enables the reduction of the background noise, the improvement of the quality of the speech signal and the generation of the least musical noise compared to three other benchmark algorithms. The development of speech detection and noise reduction algorithms, their objective and subjective evaluations in different noise environments, and their implementations in digital signal processors enabled the validation of their efficiency and low complexity for the the smart hearing protection application

Deep Scattering and End-to-End Speech Models towards Low Resource Speech Recognition

Automatic Speech Recognition (ASR) has made major leaps in its advancement largely due to two different machine learning models: Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). State-of-the art results have been achieved by combining these two disparate methods to form a hybrid system. This also requires that various components of the speech recognizer be trained independently based on a probabilistic noisy channel model. Although this HMM-DNN hybrid ASR method has been successful in recent studies, the independent development of the individual components used in hybrid HMM-DNN models makes ASR development fragile and expensive in terms of time-to-develop the various components and their associated sub-systems. The resulting trade-off is that ASR systems are difficult to develop and use especially for new applications and languages. The alternative approach, known as the end-to-end paradigm, makes use of a single deep neural-network architecture used to encapsulate as many as possible subcomponents of speech recognition as a single process. In the so-called end-to-end paradigm, latent variables of sub-components are subsumed by the neural network sub-architectures and the associated parameters. The end-to-end paradigm gains of a simplified ASR-development process again are traded for higher internal model complexity and computational resources needed to train the end-to-end models. This research focuses on taking advantage of the end-to-end model ASR development gains for new and low-resource languages. Using a specialised light weight convolution-like neural network called the deep scattering network (DSN) to replace the input layer of the end-to-end model, our objective was to measure the performance of the end-to-end model using these augmented speech features while checking to see if the light-weight, wavelet-based architecture brought about any improvements for low resource Speech recognition in particular. The results showed that it is possible to use this compact strategy for speech pattern recognition by deploying deep scattering network features with higher dimensional vectors when compared to traditional speech features. With Word Error Rates of 26.8% and 76.7% for SVCSR and LVCSR respective tasks, the ASR system metrics fell few WER points short of their respective baselines. In addition, training times tended to be longer when compared to their respective baselines and therefore had no significant improvement for low resource speech recognition training

Uses of Complex Wavelets in Deep Convolutional Neural Networks

Image understanding has long been a goal for computer vision. It has proved to be an exceptionally difficult task due to the large amounts of variability that are inherent to objects in a scene. Recent advances in supervised learning methods, particularly convolutional neural networks (CNNs), have pushed forth the frontier of what we have been able to train computers to do. Despite their successes, the mechanics of how these networks are able to recognize objects are little understood, and the networks themselves are often very difficult and time-consuming to train. It is very important that we improve our current approaches in every way possible. A CNN is built from connecting many learned convolutional layers in series. These convolutional layers are fairly crude in terms of signal processing - they are arbitrary taps of a finite impulse response filter, learned through stochastic gradient descent from random initial conditions. We believe that if we reformulate the problem, we may achieve many insights and benefits in training CNNs. Noting that modern CNNs are mostly viewed from and analyzed in the spatial domain, this thesis aims to view the convolutional layers in the frequency domain (viewing things in the frequency domain has proved useful in the past for denoising, filter design, compression and many other tasks). In particular, we use complex wavelets (rather than the Fourier transform or the discrete wavelet transform) as basis functions to reformulate image understanding with deep networks. In this thesis, we explore the most popular and well-developed form of using complex wavelets in deep learning, the ScatterNet from Stephane Mallat. We explore its current limitations by building a DeScatterNet and found that while it has many nice properties, it may not be sensitive to the most appropriate shapes for understanding natural images. We then develop a locally invariant convolutional layer, a combination of a complex wavelet transform, a modulus operation, and a learned mixing. To do this, we derive backpropagation equations and allow gradients to flow back through the (previously fixed) ScatterNet front end. Connecting several such locally invariant layers allows us to build learnable ScatterNet, a more flexible and general form of the ScatterNet (while still maintaining its desired properties). We show that the learnable ScatterNet can provide significant improvements over the regular ScatterNet when being used as a front end for a learning system. Additionally, we show that the locally invariant convolutional layer can directly replace convolutional layers in a deep CNN (and not just at the front-end). The locally invariant convolutional layers naturally downsample the input (because of the complex modulus) while increasing the channel dimension (because of the multiple wavelet orientations used). This is an operation that often happens in a CNN by a combination of a pooling and convolutional layer. It was at these locations in a CNN where the learnable ScatterNet performed best, implying it may be useful as learnable pooling layer. Finally, we develop a system to learn complex weights that act directly on the wavelet coefficients of signals, in place of a convolutional layer. We call this layer the wavelet gain layer and show it can be used alongside convolutional layers. The network designer may then choose to learn in the pixel or wavelet domains. This layer shows a lot of promise and affords more control over what regions of the frequency space we want our layer to learn from. Our experiments show that it can improve on learning in the pixel domain for early layers of a CNN