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An active filter achieving 43.6dBm OIP3

Published in:
IEEE Radio Frequency Integrated Circuits Symp., RFIC, 5-7 June 2011.

Summary

An active filter with a 50 omega buffer suitable as an anti-alias filter to drive a highly linear ADC is implemented in 0.13 um SiGe BiCMOS. This 6th-order Chebyshev filter has a 3 dB cutoff frequency of 28.3 MHz and achieves 36.5 dBm OIP3. Nonlinear digital equalization further improves OIP3 to 43.6 dBm. Measurements show 92 dB of rejection at the stopband and a gain of 49 dB. The measured in-band OIP3 of 43.6 dBm is 19 dB higher than previously published designs.
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Summary

An active filter with a 50 omega buffer suitable as an anti-alias filter to drive a highly linear ADC is implemented in 0.13 um SiGe BiCMOS. This 6th-order Chebyshev filter has a 3 dB cutoff frequency of 28.3 MHz and achieves 36.5 dBm OIP3. Nonlinear digital equalization further improves OIP3...

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Virtuoso: narrowing the semantic gap in virtual machine introspection

Published in:
2011 IEEE Symp. on Security and Privacy, 22-25 May 2011, pp. 297-312.

Summary

Introspection has featured prominently in many recent security solutions, such as virtual machine-based intrusion detection, forensic memory analysis, and low-artifact malware analysis. Widespread adoption of these approaches, however, has been hampered by the semantic gap: in order to extract meaningful information about the current state of a virtual machine, detailed knowledge of the guest operating system's inner workings is required. In this paper, we present a novel approach for automatically creating introspection tools for security applications with minimal human effort. By analyzing dynamic traces of small, in-guest programs that compute the desired introspection information, we can produce new programs that retrieve the same information from outside the guest virtual machine. We demonstrate the efficacy of our techniques by automatically generating 17 programs that retrieve security information across 3 different operating systems, and show that their functionality is unaffected by the compromise of the guest system. Our technique allows introspection tools to be effortlessly generated for multiple platforms, and enables the development of rich introspection-based security applications.
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Summary

Introspection has featured prominently in many recent security solutions, such as virtual machine-based intrusion detection, forensic memory analysis, and low-artifact malware analysis. Widespread adoption of these approaches, however, has been hampered by the semantic gap: in order to extract meaningful information about the current state of a virtual machine, detailed...

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A time-warping framework for speech turbulence-noise component estimation during aperiodic phonation

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5404-5407.

Summary

The accurate estimation of turbulence noise affects many areas of speech processing including separate modification of the noise component, analysis of degree of speech aspiration for treating pathological voice, the automatic labeling of speech voicing, as well as speaker characterization and recognition. Previous work in the literature has provided methods by which such a high-quality noise component may be estimated in near-periodic speech, but it is known that these methods tend to leak aperiodic phonation (with even slight deviations from periodicity) into the noise-component estimate. In this paper, we improve upon existing algorithms in conditions of aperiodicity by introducing a time-warping based approach to speech noise-component estimation, demonstrating the results on both natural and synthetic speech examples.
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Summary

The accurate estimation of turbulence noise affects many areas of speech processing including separate modification of the noise component, analysis of degree of speech aspiration for treating pathological voice, the automatic labeling of speech voicing, as well as speaker characterization and recognition. Previous work in the literature has provided methods...

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Assessing the speaker recognition performance of naive listeners using Mechanical Turk

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5916-5919.

Summary

In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our protocol: a series of listening experiments using large numbers of naive listeners (432) on Amazon's Mechanical Turk that attempts to measure the ability of the average human listener to perform speaker recognition. Our goal was to compare the performance of the average human listener to both forensic experts and state-of-the- art automatic systems. We show that naive listeners vary substantially in their performance, but that an aggregation of listener responses can achieve performance similar to that of expert forensic examiners.
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Summary

In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our protocol: a series of listening experiments using large numbers of naive listeners (432) on Amazon's Mechanical Turk that attempts to measure the ability...

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Informative dialect recognition using context-dependent pronunciation modeling

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 4396-4399.

Summary

We propose an informative dialect recognition system that learns phonetic transformation rules, and uses them to identify dialects. A hidden Markov model is used to align reference phones with dialect specific pronunciations to characterize when and how often substitutions, insertions, and deletions occur. Decision tree clustering is used to find context-dependent phonetic rules. We ran recognition tasks on 4 Arabic dialects. Not only do the proposed systems perform well on their own, but when fused with baselines they improve performance by 21-36% relative. In addition, our proposed decision-tree system beats the baseline monophone system in recovering phonetic rules by 21% relative. Pronunciation rules learned by our proposed system quantify the occurrence frequency of known rules, and suggest rule candidates for further linguistic studies.
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Summary

We propose an informative dialect recognition system that learns phonetic transformation rules, and uses them to identify dialects. A hidden Markov model is used to align reference phones with dialect specific pronunciations to characterize when and how often substitutions, insertions, and deletions occur. Decision tree clustering is used to find...

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NAP for high level language identification

Published in:
ICASSP 2011, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 22-27 May 2011, pp. 4392-4395.

Summary

Varying channel conditions present a difficult problem for many speech technologies such as language identification (LID). Channel compensation techniques have been shown to significantly improve performance in LID for acoustic systems. For high-level token systems, nuisance attribute projection (NAP) has been shown to perform well in the context of speaker identification. In this work, we describe a novel approach to dealing with the high dimensional sparse NAP training problem as applied to a 4-gram phonotactic LID system run on the NIST 2009 Language Recognition Evaluation (LRE) task. We demonstrate performance gains on the Voice of America (VOA) portion of the 2009 LRE data.
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Summary

Varying channel conditions present a difficult problem for many speech technologies such as language identification (LID). Channel compensation techniques have been shown to significantly improve performance in LID for acoustic systems. For high-level token systems, nuisance attribute projection (NAP) has been shown to perform well in the context of speaker...

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The MIT LL 2010 speaker recognition evaluation system: scalable language-independent speaker recognition

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 5272-5275.

Summary

Research in the speaker recognition community has continued to address methods of mitigating variational nuisances. Telephone and auxiliary-microphone recorded speech emphasize the need for a robust way of dealing with unwanted variation. The design of recent 2010 NIST-SRE Speaker Recognition Evaluation (SRE) reflects this research emphasis. In this paper, we present the MIT submission applied to the tasks of the 2010 NIST-SRE with two main goals--language-independent scalable modeling and robust nuisance mitigation. For modeling, exclusive use of inner product-based and cepstral systems produced a language-independent computationally-scalable system. For robustness, systems that captured spectral and prosodic information, modeled nuisance subspaces using multiple novel methods, and fused scores of multiple systems were implemented. The performance of the system is presented on a subset of the NIST SRE 2010 core tasks.
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Summary

Research in the speaker recognition community has continued to address methods of mitigating variational nuisances. Telephone and auxiliary-microphone recorded speech emphasize the need for a robust way of dealing with unwanted variation. The design of recent 2010 NIST-SRE Speaker Recognition Evaluation (SRE) reflects this research emphasis. In this paper, we...

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Towards reduced false-alarms using cohorts

Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 22-27 May 2011, pp. 4512-4515.

Summary

The focus of the 2010 NIST Speaker Recognition Evaluation (SRE) was the low false alarm regime of the detection error trade-off (DET) curve. This paper presents several approaches that specifically target this issue. It begins by highlighting the main problem with operating in the low-false alarm regime. Two sets of methods to tackle this issue are presented that require a large and diverse impostor set: the first set penalizes trials whose enrollment and test utterances are not nearest neighbors of each other while the second takes an adaptive score normalization approach similar to TopNorm and ATNorm.
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Summary

The focus of the 2010 NIST Speaker Recognition Evaluation (SRE) was the low false alarm regime of the detection error trade-off (DET) curve. This paper presents several approaches that specifically target this issue. It begins by highlighting the main problem with operating in the low-false alarm regime. Two sets of...

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Identification and compensation of Wiener-Hammerstein systems with feedback

Published in:
ICASSP 2011, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 22-27 May 2011, pp. 4056-4059.

Summary

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or feedback filter are more tractable but more difficult to identify. In this paper, we extend a Wiener-Hammerstein identification method to such systems showing that identification is possible (up to inherent model ambiguities) from single- and two-tone measurements. We also calculate the Cramer-Rao bound for the system parameters and compare to our identification method in simulation. Finally, we demonstrate equalization performance using measured data from a wideband GaN power amplifier.
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Summary

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or...

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Achieving cyber survivability in a contested environment using a cyber moving target

Published in:
High Frontier, Vol. 7, No. 3, May 2011, pp. 9-13.

Summary

We describe two components for achieving cyber survivability in a contested environment: an architectural component that provides heterogeneous computing platforms and an assessment technology that complements the architectural component by analyzing the threat space and triggering reorientation based on the evolving threat level. Together, these technologies provide a cyber moving target that dynamically changes the properties of the system to disadvantage the adversary and provide resiliency and survivability.
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Summary

We describe two components for achieving cyber survivability in a contested environment: an architectural component that provides heterogeneous computing platforms and an assessment technology that complements the architectural component by analyzing the threat space and triggering reorientation based on the evolving threat level. Together, these technologies provide a cyber moving...

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