A lot of effort has been made in computational auditory scene analysis (CASA) to segregate speech from monaural mixtures. The performance of current CASA systems on voiced speech segregation is limited by lacking a robust algorithm for pitch estimation. We propose a tandem algorithm that performs pitch estimation of a target utterance and segregation of voiced portions of target speech jointly and iteratively. This algorithm first obtains a rough estimate of target pitch, and then uses this estimate to segregate target speech using harmonicity and temporal continuity. It then improves both pitch estimation and voiced speech segregation iteratively. Novel methods are proposed for performing segregation with a given pitch estimate and pitch determination with given segregation. Systematic evaluation shows that the tandem algorithm extracts a majority of target speech without including much interference, and it performs substantially better than previous systems for either pitch extraction or voiced speech segregation.

The rapid development of mobile phone networks has facilitated the need for better protection against malware. Malware detection is a core component of a security system protecting mobile networks. In this paper, we describe a system for detecting malware within the network traffic using malware signatures. Our system contains two key components. The first one automatically extracts a set of signatures from existing malware samples. In particular, we reduce the number of signatures by using a common signature for a malware and its variants. In addition, we minimize the total false alarm rate of malware detection by extracting signatures that are most uncommon within mobile network traffic. The second one is an efficient method that scans the network traffic using a hash table and sub-signature matching. Our evaluation on Symbian viruses show that our system detects existing malware and their new variants within the network traffic efficiently.

A typical auditory scene in a natural environment contains multiple sources. Auditory scene analysis (ASA) is the process in which the auditory system segregates a scene into streams corresponding to different sources. Segmentation is a major stage of ASA by which an auditory scene is decomposed into segments, each containing signal mainly from one source. We propose a system for auditory segmentation by analyzing onsets and offsets of auditory events. The proposed system first detects onsets and offsets, and then generates segments by matching corresponding onset and offset fronts. This is achieved through a multiscale approach. A quantitative measure is suggested for segmentation evaluation. Systematic evaluation shows that most of target speech, including unvoiced speech, is correctly segmented, and target speech and interference are well separated into different segments

speech segregation, or the cocktail party problem, has proven to be extremely challenging. While efforts in computational auditory scene analysis have led to considerable progress in voiced speech which lacks harmonic structure and has weaker energy, hence more susceptible to interference. We describe a novel approach to address this problem. The segregation process occurs in two stages: segmentation and grouping. In segmentation, our model decomposes the input mixture into contiguous time-frequency segments by analyzing sound onsets and offsets. Grouping of unvoiced segments is based on Bayesian classification of acousticphonetic features. The proposed model yields very promising results.

In this paper, we describe a new solution for detecting mobile phone viruses. The solution is based on Bayesian decision theory using heuristic rules derived from common functionalities among different virus samples. Specifically, we detect viruses according to the DLL usage of a program, which is directly linked to the functionality of this program. Our solution is able to detect unknown viruses, especially the variants of existing ones. We evaluate our solution on the Symbian platform, where most viruses are present in the wild. We constructed a virus detector based on DLL functions from a small set of virus samples. It detects 95% of mobile viruses and yields no false alarm.

Separating speech from acoustic interference is a very challenging task. In particular, no system successfully addresses the separation of unvoiced speech. Fricatives and affricates are two main categories of consonants that contain a significant amount of unvoiced signal. We propose a novel system that separates fricatives and affricates from non-speech interference. The system first decomposes the input mixture into segments, each of which contains signal mainly from one source. Then it detects segments dominated by unvoiced portions of fricatives and affricates with a feature-based Bayesian classifier, and groups these segments with voiced speech separated by a previous system. The proposed system is evaluated with various types of interference and produces promising results.

Segregating speech from one monaural recording has proven to be very challenging. Monaural segregation of voiced speech has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with the high-frequency part of speech. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. We propose a novel system for voiced speech segregation that segregates resolved and unresolved harmonics differently. For resolved harmonics, the system generates segments based on temporal continuity and cross-channel correlation, and groups them according to their periodicities. For unresolved harmonics, it generates segments based on common amplitude modulation (AM) in addition to temporal continuity and groups them according to AM rates. Underlying the segregation process is a pitch contour that is first estimated from speech segregated according to dominant pitch and then adjusted according to psychoacoustic constraints. Our system is systematically evaluated and compared with pervious systems, and it yields substantially better performance, especially for the high-frequency part of speech.

To extract speech from acoustic interference is a challenging problem. Previous systems based on auditory scene analysis principles deal with voiced speech, but cannot separate unvoiced speech. We propose a novel method to separate stop consonants, which contain significant unvoiced signals, based on their acoustic properties. The method employs onset as the major grouping cue; it first detects stops through onset detection and feature-based Bayesian classification, then groups detected onsets based on onset coincidence. This method is tested with utterances mixed with various types of interference.

Monaural speech segregation remains a computational challenge for auditory scene analysis (ASA). A major problem for existing computational auditory scene analysis (CASA) systems is their inability to deal with signals in the high-frequency range. Psychoacoustic evidence suggests that different perceptual mechanisms are involved to handle resolved and unresolved harmonics. We propose a system for speech segregation that deals with low-frequency and high-frequency signals differently. For low-frequency signals, our model generates segments based on temporal continuity and cross-channel correlation, and groups them according to periodicity. For high-frequency signals. the model generates segments based on common amplitude modulation (AM) in addition to temporal continuity, and groups them according to AM repetition rates. Underlying the grouping process is a pitch contour that is first estimated from segregated speech based on global pitch and then verified by psychoacoustic constraints. Our system is systematically evaluated, and it yields substantially better performance than previous CASA systems, especially in the high-frequency range.

that deals with Monaural speech separation has been studied in previous systems that incorporate auditory scene analysis principles. A major problem for these systems is their inability to deal with speech in the high- frequency range. Psychoacoustic evidence suggests that different perceptual mechanisms are involved in handling resolved and unresolved harmonics. Motivated by this, we propose a model for monaural separation low-frequency and high- frequency signals differently. For resolved harmonics, our model generates segments based on temporal continuity and cross-channel correlation, and groups them according to periodicity. For unresolved harmonics, the model generates segments based on amplitude modulation (AM) in addition to temporal continuity and groups them according to AM repetition rates derived from sinusoidal modeling. Underlying the separation process is a pitch contour obtained according to psychoacoustic constraints. Our model is systematically evaluated, and it yields substantially better performance than previous systems, especially in the high-frequency range.