Sound Event Localization, Detection, and Tracking by Deep Neural Networks

In this thesis, we present novel sound representations and classification methods for the task of sound event localization, detection, and tracking (SELDT). The human auditory system has evolved to localize multiple sound events, recognize and further track their motion individually in an acoustic environment. This ability of humans makes them context-aware and enables them to interact with their surroundings naturally. Developing similar methods for machines will provide an automatic description of social and human activities around them and enable machines to be context-aware similar to humans. Such methods can be employed to assist the hearing impaired to visualize sounds, for robot navigation, and to monitor biodiversity, the home, and cities. A real-life acoustic scene is complex in nature, with multiple sound events that are temporally and spatially overlapping, including stationary and moving events with varying angular velocities. Additionally, each individual sound event class, for example, a car horn can have a lot of variabilities, i.e., different cars have different horns, and within the same model of the car, the duration and the temporal structure of the horn sound is driver dependent. Performing SELDT in such overlapping and dynamic sound scenes while being robust is challenging for machines. Hence we propose to investigate the SELDT task in this thesis and use a data-driven approach using deep neural networks (DNNs). The sound event detection (SED) task requires the detection of onset and offset time for individual sound events and their corresponding labels. In this regard, we propose to use spatial and perceptual features extracted from multichannel audio for SED using two different DNNs, recurrent neural networks (RNNs) and convolutional recurrent neural networks (CRNNs). We show that using multichannel audio features improves the SED performance for overlapping sound events in comparison to traditional single-channel audio features. The proposed novel features and methods produced state-of-the-art performance for the real-life SED task and won the IEEE AASP DCASE challenge consecutively in 2016 and 2017. Sound event localization is the task of spatially locating the position of individual sound events. Traditionally, this has been approached using parametric methods. In this thesis, we propose a CRNN for detecting the azimuth and elevation angles of multiple temporally overlapping sound events. This is the first DNN-based method performing localization in complete azimuth and elevation space. In comparison to parametric methods which require the information of the number of active sources, the proposed method learns this information directly from the input data and estimates their respective spatial locations. Further, the proposed CRNN is shown to be more robust than parametric methods in reverberant scenarios. Finally, the detection and localization tasks are performed jointly using a CRNN. This method additionally tracks the spatial location with time, thus producing the SELDT results. This is the first DNN-based SELDT method and is shown to perform equally with stand-alone baselines for SED, localization, and tracking. The proposed SELDT method is evaluated on nine datasets that represent anechoic and reverberant sound scenes, stationary and moving sources with varying velocities, a different number of overlapping sound events and different microphone array formats. The results show that the SELDT method can track multiple overlapping sound events that are both spatially stationary and moving

T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit

Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML research focuses on model compression techniques that trade accuracy (and model capacity) for compact models to fit into the KB-sized tiny-edge devices. In this paper, we show how such models can be enhanced by the addition of an early exit intermediate classifier. If the intermediate classifier exhibits sufficient confidence in its prediction, the network exits early thereby, resulting in considerable savings in time. Although early exit classifiers have been proposed in previous work, these previous proposals focus on large networks, making their techniques suboptimal/impractical for tinyML applications. Our technique is optimized specifically for tiny-CNN sized models. In addition, we present a method to alleviate the effect of network overthinking by leveraging the representations learned by the early exit. We evaluate T-RecX on three CNNs from the MLPerf tiny benchmark suite for image classification, keyword spotting and visual wake word detection tasks. Our results show that T-RecX 1) improves the accuracy of baseline network, 2) achieves 31.58% average reduction in FLOPS in exchange for one percent accuracy across all evaluated models. Furthermore, we show that our methods consistently outperform popular prior works on the tiny-CNNs we evaluate.Comment: Accepted at 20th ACM International Conference on Computing Frontier

Speech Enhancement Exploiting the Source-Filter Model

Imagining everyday life without mobile telephony is nowadays hardly possible. Calls are being made in every thinkable situation and environment. Hence, the microphone will not only pick up the user’s speech but also sound from the surroundings which is likely to impede the understanding of the conversational partner. Modern speech enhancement systems are able to mitigate such effects and most users are not even aware of their existence. In this thesis the development of a modern single-channel speech enhancement approach is presented, which uses the divide and conquer principle to combat environmental noise in microphone signals. Though initially motivated by mobile telephony applications, this approach can be applied whenever speech is to be retrieved from a corrupted signal. The approach uses the so-called source-filter model to divide the problem into two subproblems which are then subsequently conquered by enhancing the source (the excitation signal) and the filter (the spectral envelope) separately. Both enhanced signals are then used to denoise the corrupted signal. The estimation of spectral envelopes has quite some history and some approaches already exist for speech enhancement. However, they typically neglect the excitation signal which leads to the inability of enhancing the fine structure properly. Both individual enhancement approaches exploit benefits of the cepstral domain which offers, e.g., advantageous mathematical properties and straightforward synthesis of excitation-like signals. We investigate traditional model-based schemes like Gaussian mixture models (GMMs), classical signal processing-based, as well as modern deep neural network (DNN)-based approaches in this thesis. The enhanced signals are not used directly to enhance the corrupted signal (e.g., to synthesize a clean speech signal) but as so-called a priori signal-to-noise ratio (SNR) estimate in a traditional statistical speech enhancement system. Such a traditional system consists of a noise power estimator, an a priori SNR estimator, and a spectral weighting rule that is usually driven by the results of the aforementioned estimators and subsequently employed to retrieve the clean speech estimate from the noisy observation. As a result the new approach obtains significantly higher noise attenuation compared to current state-of-the-art systems while maintaining a quite comparable speech component quality and speech intelligibility. In consequence, the overall quality of the enhanced speech signal turns out to be superior as compared to state-of-the-art speech ehnahcement approaches.Mobiltelefonie ist aus dem heutigen Leben nicht mehr wegzudenken. Telefonate werden in beliebigen Situationen an beliebigen Orten geführt und dabei nimmt das Mikrofon nicht nur die Sprache des Nutzers auf, sondern auch die Umgebungsgeräusche, welche das Verständnis des Gesprächspartners stark beeinflussen können. Moderne Systeme können durch Sprachverbesserungsalgorithmen solchen Effekten entgegenwirken, dabei ist vielen Nutzern nicht einmal bewusst, dass diese Algorithmen existieren. In dieser Arbeit wird die Entwicklung eines einkanaligen Sprachverbesserungssystems vorgestellt. Der Ansatz setzt auf das Teile-und-herrsche-Verfahren, um störende Umgebungsgeräusche aus Mikrofonsignalen herauszufiltern. Dieses Verfahren kann für sämtliche Fälle angewendet werden, in denen Sprache aus verrauschten Signalen extrahiert werden soll. Der Ansatz nutzt das Quelle-Filter-Modell, um das ursprüngliche Problem in zwei Unterprobleme aufzuteilen, die anschließend gelöst werden, indem die Quelle (das Anregungssignal) und das Filter (die spektrale Einhüllende) separat verbessert werden. Die verbesserten Signale werden gemeinsam genutzt, um das gestörte Mikrofonsignal zu entrauschen. Die Schätzung von spektralen Einhüllenden wurde bereits in der Vergangenheit erforscht und zum Teil auch für die Sprachverbesserung angewandt. Typischerweise wird dabei jedoch das Anregungssignal vernachlässigt, so dass die spektrale Feinstruktur des Mikrofonsignals nicht verbessert werden kann. Beide Ansätze nutzen jeweils die Eigenschaften der cepstralen Domäne, die unter anderem vorteilhafte mathematische Eigenschaften mit sich bringen, sowie die Möglichkeit, Prototypen eines Anregungssignals zu erzeugen. Wir untersuchen modellbasierte Ansätze, wie z.B. Gaußsche Mischmodelle, klassische signalverarbeitungsbasierte Lösungen und auch moderne tiefe neuronale Netzwerke in dieser Arbeit. Die so verbesserten Signale werden nicht direkt zur Sprachsignalverbesserung genutzt (z.B. Sprachsynthese), sondern als sogenannter A-priori-Signal-zu-Rauschleistungs-Schätzwert in einem traditionellen statistischen Sprachverbesserungssystem. Dieses besteht aus einem Störleistungs-Schätzer, einem A-priori-Signal-zu-Rauschleistungs-Schätzer und einer spektralen Gewichtungsregel, die üblicherweise mit Hilfe der Ergebnisse der beiden Schätzer berechnet wird. Schließlich wird eine Schätzung des sauberen Sprachsignals aus der Mikrofonaufnahme gewonnen. Der neue Ansatz bietet eine signifikant höhere Dämpfung des Störgeräuschs als der bisherige Stand der Technik. Dabei wird eine vergleichbare Qualität der Sprachkomponente und der Sprachverständlichkeit gewährleistet. Somit konnte die Gesamtqualität des verbesserten Sprachsignals gegenüber dem Stand der Technik erhöht werden

Estudio y búusqueda de marcadores genéticos mediante el uso de Deep Neural Networks

This document contais the final dissertation ot the degree student Fernando Moreno Jabato for the studies Grade in Health Engeneering, speciality on Bioinformatics, of University of Málaga. This dissertation have been performed with the supervision of Dr. José Manuel Jerez Aragonés from the Departament of Lenguajes y Ciencias de la Comunicación. The project title is Deep Neural Networks to find genetics signatures and is focused on the development of a bioinformatic tool oriented to identification of relationships between an attribute set and concret factor of interest on medicine. To do this, a tool was designed with the capacity of handle data sets from microarrays of diferent types. Microarrays was selected as preferent technology because it's the most extended and accessible techonologie on health and biology fields nowadays. Once implemented the tool, an experiment was performed to evaluate the effciency of this tool. The experiment uses prostate cancer related datasets from trascriptomics microarrays containing patients of prostate cancer and some normal individues. The results obtained in the experiment shows an improvement offered by the new Deep Learning algoritms (specifically, Deep Neural Networks) to analyze and obtain knowledgement from microarrays data. Besides, has been observed an improvement of efficiency and the beat of computational barriers that traditional Artifical Neural Networks suffered allowing apply this bioinformatics tools of new generation to masiva data sets

Systematic Literature Review: Analisis Sentimen Berbasis Deep Learning

Systematic literature review ini bertujuan untuk mengetahui tren penelitian analisis sentimen berbasis deep learning antara tahun 2020-2023. Fokus kajiannya adalah pada pemahaman tentang pemodelan yang digunakan oleh banyak peneliti, juga nilai akurasi dari masing-masing klasifikasi tersebut. Pertanyaan utama dalam SLR ini yaitu teknik analisis sentimen berbasis deep learning apa yang memberikan akurasi tertinggi. Peneliti menemukan 400 artikel terindeks Scopus dengan menggunakan Publish or Perish 8. Selanjutnya, penyaringan jurnal dan pencarian kluster menggunakan aplikasi Microsoft Excel, Zotero, Mendeley, dan VOS Viewer yang menghasilkan 105 artikel terpilih untuk dianalisis secara deskriptif. Berdasarkan hasil temuan metode yang populer digunakan dalam melakukan analisis sentimen berbasis deep learning dalam jangka waktu yang telah ditentukan adalah metode LSTM dan CNN, baik dilakukan satu metode maupun keduanya. Adapun akurasi tertinggi mencapai 99% dengan rata-rata 89% menggunakan metode LSTM. Pengetahuan ini dapat digunakan untuk mengusulkan model analisis sentimen berbasis deep learning yang memberikan akurasi tertinggi

Transformer Meets Boundary Value Inverse Problems

A Transformer-based deep direct sampling method is proposed for a class of boundary value inverse problems. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks? Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels. Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism. The proposed method is then applied to electrical impedance tomography, a well-known severely ill-posed nonlinear inverse problem. The new method achieves superior accuracy over its predecessors and contemporary operator learners, as well as shows robustness with respect to noise. This research shall strengthen the insights that the attention mechanism, despite being invented for natural language processing tasks, offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

Dual graph convolutional networks integrating affective knowledge and position information for aspect sentiment triplet extraction

Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in natural language processing (NLP) that aims to extract triplets from comments. Each triplet comprises an aspect term, an opinion term, and the sentiment polarity of the aspect term. The neural network model developed for this task can enable robots to effectively identify and extract the most meaningful and relevant information from comment sentences, ultimately leading to better products and services for consumers. Most existing end-to-end models focus solely on learning the interactions between the three elements in a triplet and contextual words, ignoring the rich affective knowledge information contained in each word and paying insufficient attention to the relationships between multiple triplets in the same sentence. To address this gap, this study proposes a novel end-to-end model called the Dual Graph Convolutional Networks Integrating Affective Knowledge and Position Information (DGCNAP). This model jointly considers both the contextual features and the affective knowledge information by introducing the affective knowledge from SenticNet into the dependency graph construction of two parallel channels. In addition, a novel multi-target position-aware function is added to the graph convolutional network (GCN) to reduce the impact of noise information and capture the relationships between potential triplets in the same sentence by assigning greater positional weights to words that are in proximity to aspect or opinion terms. The experiment results on the ASTE-Data-V2 datasets demonstrate that our model outperforms other state-of-the-art models significantly, where the F1 scores on 14res, 14lap, 15res, and 16res are 70.72, 57.57, 61.19, and 69.58

BiTCAN: An emotion recognition network based on saliency in brain cognition

In recent years, with the continuous development of artificial intelligence and brain-computer interfaces, emotion recognition based on electroencephalogram (EEG) signals has become a prosperous research direction. Due to saliency in brain cognition, we construct a new spatio-temporal convolutional attention network for emotion recognition named BiTCAN. First, in the proposed method, the original EEG signals are de-baselined, and the two-dimensional mapping matrix sequence of EEG signals is constructed by combining the electrode position. Second, on the basis of the two-dimensional mapping matrix sequence, the features of saliency in brain cognition are extracted by using the Bi-hemisphere discrepancy module, and the spatio-temporal features of EEG signals are captured by using the 3-D convolution module. Finally, the saliency features and spatio-temporal features are fused into the attention module to further obtain the internal spatial relationships between brain regions, and which are input into the classifier for emotion recognition. Many experiments on DEAP and SEED (two public datasets) show that the accuracies of the proposed algorithm on both are higher than 97%, which is superior to most existing emotion recognition algorithms