Pathological speech classification using a convolutional neural network

Convolutional Neural Networks (CNNs) have enabled significant improvements across a number of applications in computer vision such as object detection, face recognition and image classification. An audio signal can be visually represented as a spectrogram that captures the time-varying frequency content of the signal. This paper describes how a CNN can be applied to the spectrogram of an audio signal to distinguish pathological from healthy speech. We propose a CNN structure and implement it using Keras to test the approach. A classification accuracy of over 95% is obtained in experiments on two public pathological speech datasets

Detection of malicious VBA macros using machine learning methods

Since their appearance in 1994 in the Concept virus, VBA macros remain a preferred choice for malware authors. There are two main attack techniques when it comes to document-based malware: exploits and VBA macros, with the latter applied in the vast majority of threats. Although Microsoft have added multiple security features in an attempt to protect users against malicious macros, such protections are often easily circumvented by simple social engineering techniques. Anti-virus companies can no longer rely on static signatures due to the rate at which new macro malware is distributed, and thus are tasked with employing a more proactive approach to threat detection. This paper details the literature on machine learning methods for the detection of VBA macro malware. Further, a machine learning system for the detection of VBA macro malware is proposed and evaluated. A Random Forest classifier achieves a true positive detection rate of 98.9875% with a false positive detection rate of 1.07% over a set of 611 mixed (benign and malicious) malware samples

DNS Traffic analysis for botnet detection

Botnets pose a major threat to cyber security. Given that firewalls typically prevent unsolicited incoming traffic from reaching hosts internal to the local area network, it is up to each bot to initiate a connection with its remote Command and Control (C&C) server. To perform this task a bot can use either a hardcoded IP address or perform a DNS lookup for a predefined or algorithmically-generated domain name. Modern malware increasingly utilizes DNS to enhance the overall availability and reliability of the C&C communication channel. In this paper we present a prototype botnet detection system that leverages passive DNS traffic analysis to detect a botnet’s presence in a local area network. A naive Bayes classifier is trained on features extracted from both benign and malicious DNS traffic traces and its performance is evaluated. Since the proposed method relies on DNS traffic, it permits the early detection of bots on the network. In addition, the method does not depend on the number of bots operating in the local network and is effective when only a small number of infected machines are present

Semi-supervised learning with generative adversarial networks for pathological speech classification

One application of deep learning in medical applications is the use of deep neural networks to classify human speech as healthy or pathological. In such applications, the audio signal is transformed into a spectrogram that captures its time-varying content and the latter “images” are fed into a classifier for classification. A challenge in applying this approach is the shortage of suitable speech data for training purposes. Labelled data acquisition requires significant human effort and/or time-consuming experiments. In this paper, we propose a semi-supervised learning approach that employs a Generative Adversarial Network (GAN) to alleviate the problem of insufficient training data. We compare the classification performance of a traditional classifier and our semi-supervised classifier. We observe that the GAN-based semi-supervised approach demonstrates a significant improvement in terms of accuracy and ROC curve when supplied an equivalent number of training samples

Preliminary steps toward artificial protocell computation

Protocells are hypothesised as a transitional phase in the origin of life, prior to the evolution of fully functional prokaryotic cells. The work reported here is being done in the context of the PACE project, which is investigating the fabrication of artificial protocells de novo. We consider here the important open question of whether or how articifial protocells (if or when they are successfully fabricated) might be applied as “computing” devices—what sort of computing might they be suitable for, and how might they be “programmed”? We also present some preliminary analysis of a crude model of such “evolutionary protocell computation”

Speech synthesis based on a harmonic model

The wide range of potential commercial applications for a com puter system capable of automatically converting text to speech (TTS) has stimulated decades of research. One of the currently most successful approaches to synthesising speech, concatenative TTS synthesis, combines prerecorded speech units to build full utterances. However, th e prosody of the stored units is often not consistent with that of the target utterance and m ust be altered. Furthermore, several types of mismatch can occur at unit boundaries and must be smoothed. Thus, pitch and time-scale modification techniques as well as smoothing algorithms play a critical role in all concatenative-based systems. This thesis presents the developm ent of a concatenative TTS system based on a harm onic model and incorporating new pitch and time-scaling as well as smoothing algorithms. Experim ent has shown our system capable of both very high quality prosodic modification and synthesis. Results com pare very favourably with those of existing state-of-the-art systems

On Protocell "Computation"

The EU FP6 Integrated Project PACE ('Programmable Artificial Cell Evolution') is investigating the creation, de novo, of chemical 'protocells'. These will be minimal 'wetware' chemical systems integrating molecular information carriers, primitive energy conversion (metabolism) and containment (membrane). Ultimately they should be capable of autonomous reproduction, and be 'programmable' to realise specific desired function. A key objective of PACE is to explore the application of such protocell technology to build novel nanoscale computational devices. In principle, such computation might be realised either at the level of an individual protocell or at the level of self-assembling, multi-cellular, aggregates. In the case of the individual protocell level, a form of 'molecular computation' may be possible in the manner of 'cell signalling networks' in modern cells. This might be particularly appropriate where a protocell is deployed to interface directly with molecular systems, such as in 'smart drug' applications. 'Programming' of molecular computation functionality might be realised by evolutionary techniques, i.e., applying selection to polulations of (reproducing) protocells. Reflexive string rewriting systems may provide an appropriate formal model of molecular computation. The behaviour of minimal reflexive string rewriting systems, incorporated in reproducing containers (protocells), is being explored in simulation. This is a basis for possible design of minimal protocell 'computers'

Cellular computation using classifier systems

The EU FP6 Integrated Project PACE ('Programmable Artificial Cell Evolution') is investigating the creation, de novo, of chemical 'protocells'. These will be minimal 'wetware' chemical systems integrating molecular information carriers, primitive energy conversion (metabolism) and containment (membrane). Ultimately they should be capable of autonomous reproduction, and be 'programmable' to realise specific desired function. A key objective of PACE is to explore the application of such protocell technology to build novel nanoscale computational devices. Our contribution to this project is to investigate approaches to adding minimal computational capability to protocells. We introduce the Molecular Classifier System (MCS) to represent the internal molecular reactions of the protocell. Reactions in the MCS are constrained as follows: The products of the reaction depend on the reactants and the environment in which the reaction took place; The reactions that can happen depend on the physical and chemical structure of the reacting compounds. In our MCS, there are reactants and reaction rules. The rules determine the reactants and the products for a given interaction. These simple computational processes may also help in understanding the origins of Cell Signaling Networks(CSNs). CSNs are complex bio-chemical networks responsible for coordinating and controlling cellular activities. CSNs can therefore be regarded as computational systems. To understand the evolution of such complex computational systems as found in nature, we will distinguish the minimal computational properties fundamental for the survival of a protocell

Automatic LF-model fitting to the glottal source waveform by extended Kalman filtering

A new method for automatically fitting the Liljencrants-Fant (LF) model to the time domain waveform of the glottal flow derivative is presented in this paper. By applying an extended Kalman filter (EKF) to track the LF-model shape-controlling parameters and dynamically searching for a globally minimal fitting error, the algorithm can accurately fit the LF-model to the inverse filtered glottal flow derivative. Experimental results show that the method has better performance for both synthetic and real speech signals compared to a standard time-domain LF-model fitting algorithm. By offering a new method to estimate the glottal source LF-model parameters, the proposed algorithm can be utilised in many applications

Robust tracking of glottal LF-model parameters by multi-estimate fusion

A new approach to robust tracking of glottal LF-model parameters is presented. The approach does not rely on a new glottal source estimation algorithm, but instead introduces a new extensible multi-estimate fusion framework. Within this framework several existing algorithms are applied in parallel to extract glottal LF-model parameter estimates which are subsequently passed to quantitative data fusion procedures. The preliminary implementation of the fusion algorithm described here incorporates three glottal inverse filtering methods and one time-domain LF-model fitting algorithm. Experimental results for both synthetic and natural speech signals demonstrate the effectiveness of the fusion algorithm. The proposed method is flexible and can be easily extended for other speech processing applications such as speech synthesis, speaker identification and prosody analysis