DOCSLIB.ORG

Self-Supervised Adversarial Multi-Task Learning for Vocoder-Based Monaural Speech Enhancement

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Self-Supervised Adversarial Multi-Task Learning for Vocoder-Based Monaural Speech Enhancement Self-supervised Adversarial Multi-task Learning for Vocoder-based Monaural Speech Enhancement Zhihao Du1, Ming Lei2, Jiqing Han1, Shiliang Zhang2 1School of Computer Science and Technology, Harbin Institute of Technology 2Speech Lab, Alibaba DAMO Academy duzhihao,jqhan @hit.edu.cn, lm86501,sly.zsl @alibaba-inc.com { } { } Abstract to monaural speech enhancement in our previous study [13]. Specifically, a denoising autoencoder is trained to reconstruct In our previous study, we introduce the neural vocoder into the clean Mel power spectrum, and a flow-based generative monaural speech enhancement, in which a flow-based gener- vocoder, Flowavenet [14], is employed to synthesize the speech ative vocoder is used to synthesize speech waveforms from the waveform from the enhanced features without using the noisy Mel power spectra enhanced by a denoising autoencoder. As phase spectrum or predicting a clean one. As a result, vocoder- a result, this vocoder-based enhancement method outperforms based enhancement methods bypass the problem of phase pre- several state-of-the-art models on a speaker-dependent dataset. diction and outperform several state-of-the-art models on a However, we find that there is a big gap between the enhance- speaker-dependent dataset [13]. ment performance on the trained and untrained noises. There- According to the results in our previous study, we find fore, in this paper, we propose the self-supervised adversarial that there is a big gap between the enhancement perfor- multi-task learning (SAMLE) to improve the noise generaliza- mance on trained and untrained noises. Therefore, in this tion ability. In addition, the speaker dependence is also eval- paper, we propose the self-supervised adversarial multi-task uated for the vocoder-based methods, which is important for learning (SAMLE) to improve the noise generalization ability real-life applications. Experimental results show that the pro- for vocoder-based enhancement methods. In addition, since posed SAMLE further improves the enhancement performance vocoders are originally designed to synthesize waveforms for on both trained and untrained noises, resulting in a better noise a specific speaker, previous vocoder-based enhancement meth- generalization ability. Moreover, we find that vocoder-based ods are always trained and evaluated on a speaker-dependent enhancement methods can be speaker-independent through a dataset [13, 15, 16]. However, enhancement methods should large-scale training. be speaker independent in real-life applications, i.e. speakers Index Terms: self-supervised, multi-task learning, vocoder- for training and test are different. Therefore, we also evalu- based speech enhancement ate the speaker dependence of flow-based generative vocoder (Flowavenet) [14] and autoregressive vocoder (WaveRNN) [17] 1. Introduction for both the speech synthesis and enhancement tasks. Monaural speech enhancement aims at separating speeches from the noisy backgrounds by using a single microphone. 2. Vocoder-based enhancement method Since it was formulated as a supervised learning problem, The proposed method consists of a denoising autoencoder monaural speech enhancement has achieved a huge progress by (DAE), an autoregressive vocoder and a noise classifier. As using the deep learning techniques [1]. Early supervised meth- shown in Fig.1, DAE attempts to reconstruct the clean Mel ods only enhance the magnitude spectrum of noisy speeches but power spectrum from the noisy complex spectrum, and the au- leave the noisy phase spectrum unchanged [2], such as the ideal toregressive vocoder is used to synthesize the speech waveform ratio mask (IRM) [3] and spectral mapping [4]. Recent studies from the predicted Mel power spectrum. Meanwhile, a noise suggest that the phase spectrum is also important for perceptual classifier is involved to perform the self-supervised adversarial quality and speech intelligibility [5]. Therefore, many studies multi-task learning (SAMLE). As in [13], we first train the DAE are making efforts on enhancing the magnitude and phase spec- and vocoder separately, and then stack them together. tra simultaneously. In the complex spectrum domain, the mag- nitude and phase spectra are enhanced by estimating the com- 2.1. Denoising autoencoder plex ratio mask (CRM) [6] or their clean counterparts [7, 8]. In the waveform domain, the WaveNet [9] and Wave-U-Net [10] In DAE, the encoder is trained to learn an intermediate repre- are introduced and trained with the energy-conserving loss [11] sentation h from the complex spectrum of noisy speech, and and frequency-domain loss [12], respectively. the decoder aims at reconstructing the clean Mel power spec- In general, it is more difficult to enhance the complex spec- trum from h. The mean absolute error (MAE) is employed as trum and waveform than the magnitude spectrum due to the the loss function, which is defined between the predicted Mel ˆ lack of clear structures in them. Inspired by the progress in power spectrum S and the clean one S: speech synthesis community, we introduce the neural vocoder T F 1 1 MAE(S, Sˆ)= S(t, f) Sˆ(t, f) (1) This work was performed while the first author was an intern at L T F − Speech Lab, Alibaba DAMO Academy. This research was supported t=1 f=1 X X by National Key Research and Development Program of China un- der Grant 2017YFB1002102, National Natural Science Foundation of where T and F indicate the total numbers of time frames and China under Grant U1736210. frequency bins, respectively. means the absolute value. An- |·| Figure 1: (Color online) An overview of the proposed vocoder-based enhancement method. The solid and dashed arrows indicate the forward and backward passes, respectively. other optional choice here is mean square error (MSE), how- tains slightly better results. Therefore, we employ the second ever, previous studies show that MAE leads to higher speech way in our following experiments. intelligibility and perceptual quality than MSE [18]. Different from our previous study [13], we feed the DAE 2.2.2. Self-supervised noise classification with noisy complex spectra rather than the noisy Mel power To perform the adversarial multi-task learning, we need to ar- spectra. The complex spectrum can benefit speech enhancement range the noise category to each noisy utterance. However, it from two respects. First, it contains not only the magnitude is expensive and time-consuming to label thousands of noisy information but also the phases. By using the phase features, utterances manually. In addition, a noise can be arranged with such as group delay, the performance of speech recognition and several categories from different respects, which is another ob- speaker identification has been improved [19, 20]. Therefore, stacle to manually noise labeling. Therefore, we propose a self- the complex spectrum may provide another cue (phase informa- supervised noise classification method by developing an auto- tion) for speech enhancement. Second, the complex spectrum matic noise labeling criterion. According to the energy dis- has higher frequency resolution than the Mel power spectrum, tribution in the frequency domain, we divide noises into three which provides more details for speech enhancement. categories, i.e. “low-frequency”, “high-frequency” and “full- frequency” noises. Formally, the labeling criterion is defined as 2.2. Self-supervised adversarial multi-task learning follows: 2.2.1. Adversarial multi-task learning 0 Pl > Pa/2 Only trained with the reconstruction loss MAE, DAE may suf- c := 1 P P /2 (3) L 8 h > a fer the performance degradation on untrained noises, because <>2 otherwise the intermediate representation h is not restricted, which may T ↵F b c contain the information of background noises. To overcome this :> 2 Pl := N (t, f) (4) problem, an additional task, noise classification, is added to the | | t=1 DAE, resulting in a multi-task learning scheme. Specifically, a X fX=1 T F noise classifier C is involved, which is trained to distinguish 2 Ph := N (t, f) (5) the noise categories according to the intermediate representa- | | t=1 f= βF tions by minimizing the cross entropy: X Xb c T F K 2 (1) (2) (T ) Pa := N (t, f) (6) CE(c, C (h)) = cik log C h ,h ,...,h | | L k i i i t=1 f=1 k=1 X X X ⇣ ⌘ 2 (2) where N is the power spectrum of a noise. Pl, Ph and Pa where, K and T are the total numbers of noise categories and represent| | the total energy in the low, high and all frequency- time frames. ci is a one-hot code of the noise category for the bins, respectively. ↵ and β are hyper-parameters to adjust the (t) noisy utterance i. hi means the intermediate representation range of low and high frequency-bins (0 <↵6 β<1). of utterance i at frame t. While the noise classifier attempts By applying the self-supervised noise classification to the to minimize the cross entropy loss CE, the encoder tries to adversarial multi-task learning, we obtain the self-supervised maximize it by adjusting the intermediateL representation of each adversarial multi-task learning (SAMLE), and its optimizing frame. Through the adversarial multi-task learning, the interme- target is given as follows: diate representation can be noise-invariant. min max MAE(S, D'(E✓(X))) λ CE(c, C (E✓(X))) There are two ways to implement the adversarial training. ✓,' L − L One is adding a gradient reverse layer (GRL) [21] between (7) the encoder and noise classifier, like domain adaption methods where λ is a hyper-parameter to balance the feature reconstruc- [22, 23]. The other way is updating the encoder and noise clas- tion and noise classification. Through SAMLE, the intermedi- sifier iteratively, like the generative adversarial networks [24]. ate representations can be noise-invariant, which improves the In our preliminary experiments, we find that the latter one ob- noise generalization ability of DAE. 2.3.
Load more

Recommended publications
  • Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
    PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction
    [Show full text]
  • Almost Unsupervised Text to Speech and Automatic Speech Recognition
    Almost Unsupervised Text to Speech and Automatic Speech Recognition Yi Ren * 1 Xu Tan * 2 Tao Qin 2 Sheng Zhao 3 Zhou Zhao 1 Tie-Yan Liu 2 Abstract 1. Introduction Text to speech (TTS) and automatic speech recognition (ASR) are two popular tasks in speech processing and have Text to speech (TTS) and automatic speech recog- attracted a lot of attention in recent years due to advances in nition (ASR) are two dual tasks in speech pro- deep learning. Nowadays, the state-of-the-art TTS and ASR cessing and both achieve impressive performance systems are mostly based on deep neural models and are all thanks to the recent advance in deep learning data-hungry, which brings challenges on many languages and large amount of aligned speech and text data. that are scarce of paired speech and text data. Therefore, However, the lack of aligned data poses a ma- a variety of techniques for low-resource and zero-resource jor practical problem for TTS and ASR on low- ASR and TTS have been proposed recently, including un- resource languages. In this paper, by leveraging supervised ASR (Yeh et al., 2019; Chen et al., 2018a; Liu the dual nature of the two tasks, we propose an et al., 2018; Chen et al., 2018b), low-resource ASR (Chuang- almost unsupervised learning method that only suwanich, 2016; Dalmia et al., 2018; Zhou et al., 2018), TTS leverages few hundreds of paired data and extra with minimal speaker data (Chen et al., 2019; Jia et al., 2018; unpaired data for TTS and ASR.
    [Show full text]
  • Self-Supervised Learning
    Self-Supervised Learning Andrew Zisserman Slides from: Carl Doersch, Ishan Misra, Andrew Owens, Carl Vondrick, Richard Zhang The ImageNet Challenge Story … 1000 categories • Training: 1000 images for each category • Testing: 100k images The ImageNet Challenge Story … strong supervision The ImageNet Challenge Story … outcomes Strong supervision: • Features from networks trained on ImageNet can be used for other visual tasks, e.g. detection, segmentation, action recognition, fine grained visual classification • To some extent, any visual task can be solved now by: 1. Construct a large-scale dataset labelled for that task 2. Specify a training loss and neural network architecture 3. Train the network and deploy • Are there alternatives to strong supervision for training? Self-Supervised learning …. Why Self-Supervision? 1. Expense of producing a new dataset for each new task 2. Some areas are supervision-starved, e.g. medical data, where it is hard to obtain annotation 3. Untapped/availability of vast numbers of unlabelled images/videos – Facebook: one billion images uploaded per day – 300 hours of video are uploaded to YouTube every minute 4. How infants may learn … Self-Supervised Learning The Scientist in the Crib: What Early Learning Tells Us About the Mind by Alison Gopnik, Andrew N. Meltzoff and Patricia K. Kuhl The Development of Embodied Cognition: Six Lessons from Babies by Linda Smith and Michael Gasser What is Self-Supervision? • A form of unsupervised learning where the data provides the supervision • In general, withhold some part of the data, and task the network with predicting it • The task defines a proxy loss, and the network is forced to learn what we really care about, e.g.
    [Show full text]
  • Reinforcement Learning in Supervised Problem Domains
    Technische Universität München Fakultät für Informatik Lehrstuhl VI – Echtzeitsysteme und Robotik reinforcement learning in supervised problem domains Thomas F. Rückstieß Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Daniel Cremers Prüfer der Dissertation 1. Univ.-Prof. Dr. Patrick van der Smagt 2. Univ.-Prof. Dr. Hans Jürgen Schmidhuber Die Dissertation wurde am 30. 06. 2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 18. 09. 2015 angenommen. Thomas Rückstieß: Reinforcement Learning in Supervised Problem Domains © 2015 email: [email protected] ABSTRACT Despite continuous advances in computing technology, today’s brute for- ce data processing approaches may not provide the necessary advantage to win the race against the ever-growing amount of data that can be wit- nessed over the last decades. In this thesis, we discuss novel methods and algorithms that are capable of directing attention to relevant details and analysing it in sequence to overcome the processing bottleneck and to keep up with this data explosion. In the first of three parts, a novel exploration technique for Policy Gradi- ent Reinforcement Learning is presented which replaces traditional ad- ditive random exploration with state-dependent exploration, exploring on a higher, more strategic level. We will show how this new exploration method converges faster and finds better global solutions than random exploration can. The second part of this thesis will introduce the concept of “data con- sumption” and discuss means to minimise it in supervised learning tasks by deriving classification as a sequential decision process and ma- king it accessible to Reinforcement Learning methods.
    [Show full text]
  • Infinite Variational Autoencoder for Semi-Supervised Learning
    Infinite Variational Autoencoder for Semi-Supervised Learning M. Ehsan Abbasnejad Anthony Dick Anton van den Hengel The University of Adelaide {ehsan.abbasnejad, anthony.dick, anton.vandenhengel}@adelaide.edu.au Abstract with whatever labelled data is available to train a discrimi- native model for classification. This paper presents an infinite variational autoencoder We demonstrate that our infinite VAE outperforms both (VAE) whose capacity adapts to suit the input data. This the classical VAE and standard classification methods, par- is achieved using a mixture model where the mixing coef- ticularly when the number of available labelled samples is ficients are modeled by a Dirichlet process, allowing us to small. This is because the infinite VAE is able to more ac- integrate over the coefficients when performing inference. curately capture the distribution of the unlabelled data. It Critically, this then allows us to automatically vary the therefore provides a generative model that allows the dis- number of autoencoders in the mixture based on the data. criminative model, which is trained based on its output, to Experiments show the flexibility of our method, particularly be more effectively learnt using a small number of samples. for semi-supervised learning, where only a small number of The main contribution of this paper is twofold: (1) we training samples are available. provide a Bayesian non-parametric model for combining autoencoders, in particular variational autoencoders. This bridges the gap between non-parametric Bayesian meth- 1. Introduction ods and the deep neural networks; (2) we provide a semi- supervised learning approach that utilizes the infinite mix- The Variational Autoencoder (VAE) [18] is a newly in- ture of autoencoders learned by our model for prediction troduced tool for unsupervised learning of a distribution x x with from a small number of labeled examples.
    [Show full text]
  • Unsupervised Speech Representation Learning Using Wavenet Autoencoders Jan Chorowski, Ron J
    1 Unsupervised speech representation learning using WaveNet autoencoders Jan Chorowski, Ron J. Weiss, Samy Bengio, Aaron¨ van den Oord Abstract—We consider the task of unsupervised extraction speaker gender and identity, from phonetic content, properties of meaningful latent representations of speech by applying which are consistent with internal representations learned autoencoding neural networks to speech waveforms. The goal by speech recognizers [13], [14]. Such representations are is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being desired in several tasks, such as low resource automatic speech invariant to confounding low level details in the signal such as recognition (ASR), where only a small amount of labeled the underlying pitch contour or background noise. Since the training data is available. In such scenario, limited amounts learned representation is tuned to contain only phonetic content, of data may be sufficient to learn an acoustic model on the we resort to using a high capacity WaveNet decoder to infer representation discovered without supervision, but insufficient information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the to learn the acoustic model and a data representation in a fully kind of constraint that is applied to the latent representation. supervised manner [15], [16]. We compare three variants: a simple dimensionality reduction We focus on representations learned with autoencoders bottleneck, a Gaussian Variational Autoencoder (VAE), and a applied to raw waveforms and spectrogram features and discrete Vector Quantized VAE (VQ-VAE). We analyze the quality investigate the quality of learned representations on LibriSpeech of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately re- [17].
    [Show full text]
  • A Primer on Machine Learning
    A Primer on Machine Learning By instructor Amit Manghani Question: What is Machine Learning? Simply put, Machine Learning is a form of data analysis. Using algorithms that “ continuously learn from data, Machine Learning allows computers to recognize The core of hidden patterns without actually being programmed to do so. The key aspect of Machine Learning Machine Learning is that as models are exposed to new data sets, they adapt to produce reliable and consistent output. revolves around a computer system Question: consuming data What is driving the resurgence of Machine Learning? and learning from There are four interrelated phenomena that are behind the growing prominence the data. of Machine Learning: 1) the ever-increasing volume, variety and velocity of data, 2) the decrease in bandwidth and storage costs and 3) the exponential improve- ments in computational processing. In a nutshell, the ability to perform complex ” mathematical computations on big data is driving the resurgence in Machine Learning. 1 Question: What are some of the commonly used methods of Machine Learning? Reinforce- ment Machine Learning Supervised Machine Learning Semi- supervised Machine Unsupervised Learning Machine Learning Supervised Machine Learning In Supervised Learning, algorithms are trained using labeled examples i.e. the desired output for an input is known. For example, a piece of mail could be labeled either as relevant or junk. The algorithm receives a set of inputs along with the corresponding correct outputs to foster learning. Once the algorithm is trained on a set of labeled data; the algorithm is run against the same labeled data and its actual output is compared against the correct output to detect errors.
    [Show full text]
  • 4 Perceptron Learning
    4 Perceptron Learning 4.1 Learning algorithms for neural networks In the two preceding chapters we discussed two closely related models, McCulloch–Pitts units and perceptrons, but the question of how to find the parameters adequate for a given task was left open. If two sets of points have to be separated linearly with a perceptron, adequate weights for the comput- ing unit must be found. The operators that we used in the preceding chapter, for example for edge detection, used hand customized weights. Now we would like to find those parameters automatically. The perceptron learning algorithm deals with this problem. A learning algorithm is an adaptive method by which a network of com- puting units self-organizes to implement the desired behavior. This is done in some learning algorithms by presenting some examples of the desired input- output mapping to the network. A correction step is executed iteratively until the network learns to produce the desired response. The learning algorithm is a closed loop of presentation of examples and of corrections to the network parameters, as shown in Figure 4.1. network test input-output compute the examples error fix network parameters Fig. 4.1. Learning process in a parametric system R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996 78 4 Perceptron Learning In some simple cases the weights for the computing units can be found through a sequential test of stochastically generated numerical combinations. However, such algorithms which look blindly for a solution do not qualify as “learning”. A learning algorithm must adapt the network parameters accord- ing to previous experience until a solution is found, if it exists.
    [Show full text]
  • Beyond Word Embeddings: Dense Representations for Multi-Modal Data
    The Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS-32) Beyond Word Embeddings: Dense Representations for Multi-Modal Data Luis Armona,1,2 José P. González-Brenes,2∗ Ralph Edezhath2∗ 1Department of Economics, Stanford University, 2Chegg, Inc. [email protected], {redezhath, jgonzalez}@chegg.com Abstract was required to extend a model like Word2Vec to support additional features. The authors of the seminal Doc2Vec (Le Methods that calculate dense vector representations for text have proven to be very successful for knowledge represen- and Mikolov 2014) paper needed to design both a new neural tation. We study how to estimate dense representations for network and a new sampling strategy to add a single feature multi-modal data (e.g., text, continuous, categorical). We (document ID). Feat2Vec is a general method that allows propose Feat2Vec as a novel model that supports super- calculating embeddings for any number of features. vised learning when explicit labels are available, and self- supervised learning when there are no labels. Feat2Vec calcu- 2 Feat2Vec lates embeddings for data with multiple feature types, enforc- In this section we describe how Feat2Vec learns embed- ing that all embeddings exist in a common space. We believe that we are the first to propose a method for learning self- dings of feature groups. supervised embeddings that leverage the structure of multiple feature types. Our experiments suggest that Feat2Vec outper- 2.1 Model forms previously published methods, and that it may be use- Feat2Vec predicts a target output y~ from each observation ful for avoiding the cold-start problem. ~x, which is multi-modal data that can be interpreted as a list of feature groups ~gi: 1 Introduction ~x = h~g1;~g2; : : : ;~gni = h~x~κ1 ; ~x~κ2 ; : : : ; ~x~κn i (1) Informally, in machine learning a dense representation, or For notational convenience, we also refer to the i-th feature n ~ r group as ~x .
    [Show full text]
  • Introducing Machine Learning for Healthcare Research
    INTRODUCING MACHINE LEARNING FOR HEALTHCARE RESEARCH Dr Stephen Weng NIHR Research Fellow (School for Primary Care Research) Primary Care Stratified Medicine (PRISM) Division of Primary Care School of Medicine University of Nottingham What is Machine Learning? Machine learning teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computation methods to “learn” information directly from data without relying on a predetermined equation to model. The algorithms adaptively improve their performance as the number of data samples available for learning increases. When Should We Use Considerations: Complex task or problem Machine Learning? Large amount of data Lots of variables No existing formula or equation Limited prior knowledge Hand-written rules and The nature of input and quantity Rules of the task are dynamic – equations are too complex – of data keeps changing – hospital financial transactions images, speech, linguistics admissions, health care records Supervised learning, which trains a model on known inputs and output data to predict future outputs How Machine Learning Unsupervised learning, which finds hidden patterns or Works intrinsic structures in the input data Semi-supervised learning, which uses a mixture of both techniques; some learning uses supervised data, some learning uses unsupervised learning Unsupervised Learning Group and interpret data Clustering based only on input data Machine Learning Classification Supervised learning Develop model based
    [Show full text]
  • Combining Supervised and Unsupervised Machine Learning
    Darktrace AI: Combining Unsupervised and Supervised Machine Learning Technical White Paper A New Age of Cyber-Threat Executive Summary A Changing Threat Landscape Darktrace is a research and development-led company that first applied The last decade has seen an unmistakable escalation in cyber conflict, as criminals, unsupervised machine learning to the challenge of detecting threatening nation states and lone opportunists take advantage of digitization and connectivity to digital activity within corporate networks, with the aim of combatting novel compromise networks and gain advantage – whether financial, reputational, or strategic. cyber attacks that bypass rule-based security tools. This white paper Our adversaries in cyber space are constantly innovating too, launching new attack examines the multiple layers of machine learning that make up Darktrace’s tools and technologies to get through traditional cyber security defenses, such as Cyber AI, and how they are architected together to create an autonomous, firewalls and signature-based gateways, and into the file shares or accounts that are system that self-updates, responding to, but not requiring, human input. most valuable to them. This system is today used by over 4,000 organizations globally. Attackers have also exploited to their advantage the digital complexity of the average organization of today, which shares data across multiple locations, devices and technology services– from cloud services and SaaS tools, to untrusted home networks and non-official IoT devices. Meanwhile, malicious insiders remain a constant threat. The new age of cyber security is defined by a constantly evolving threat landscape: no sooner have you identified the adversary, than it has shape-shifted into something unrecognizable.
    [Show full text]
  • Supervised Learning in Neural Networks
    Supervised Learning in Neural Networks April 11, 2010 1 Introduction Supervised Learning scenarios arise reasonably seldom in real life. They are situations in which an agent (e.g. human) performs many similar tasks, or many similar steps of a longer task, and receives frequent and detailed feedback: after each step, the agent learns a) whether or not her action was correct, and, in addition, b) the action that should have been performed. In real life, this type of feedback is not only rare, but annoying. Few people wish to have their every move corrected. In Machine Learning (ML), however, this learning paradigm has generated a lot of research interest for decades. The classic tasks involve classification, wherein the system is given a large data set, with each case consisting of many features plus a classification. For example, the features might be meteorological factors such as temperature, humidity, wind velocity, etc., while the class could be the predicted precipitation level (high, medium, low, or zero) for the next day. The supervised-learning classifier system must then learn to predict future precipitation when given a vector of meteorological features. The system receives the input features for a case and produces a precipitation prediction, whose accuracy can be assessed by a comparison to the known precipitation attached to that case. Any discrepancy between the two can then serve as an error term for modifying the system (so as to make better predictions in the future). Classification tasks are a staple of Machine Learning, and artificial neural networks are one of several standard tools (including decision-tree learning, case-based reasoning and Bayesian methods) used to tackle them.
    [Show full text]