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RISS 인기검색어
- 검색키워드
- 저자 : Hyeontaek Lim (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Speaker localization in noisy environments using steered response voice power
Lim, Hyeontaek,Yoo, In-Chul,Cho, Youngkyu,Yook, Dongsuk IEEE 2015 IEEE transactions on consumer electronics Vol.61 No.1
<P>Many devices, including smart TVs and humanoid robots, can be operated through speech interface. Since a user can interact with such a device at a distance, speech-operated devices must be able to process speech signals from a distance. Although many methods exist to localize speakers via sound source localization, it is very difficult to reliably find the location of a speaker in a noisy environment. In particular, conventional sound source localization methods only find the loudest sound source within a given area, and such a sound source may not necessarily be related to human speech. This can be problematic in real environments where loud noises frequently occur, and the performance of speech-based interfaces for a variety of devices could be negatively impacted as a result. In this paper, a new speaker localization method is proposed. It identifies the location associated with the maximum voice power from all candidate locations. The proposed method is tested under a variety of conditions using both simulation data and real data, and the results indicate that the performance of the proposed method is superior to that of a conventional algorithm for various types of noises<SUP>1</SUP>.</P>
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Achieving One Billion Key-Value Requests per Second on a Single Server
Sheng Li,Hyeontaek Lim,Lee, Victor W.,Jung Ho Ahn,Kalia, Anuj,Kaminsky, Michael,Andersen, David G.,Seongil O,Sukhan Lee,Dubey, Pradeep IEEE 2016 IEEE micro Vol.36 No.3
<P>Distributed in-memory key-value stores (KVSs) have become a critical data-serving layer in cloud computing and big data infrastructure. Unfortunately, KVSs have demonstrated a gap between achieved and available performance, QoS, and energy efficiency on commodity platforms. Two research thrusts have focused on improving key-value performance: hardware-centric research has started to explore specialized platforms for KVSs, and software-centric research revisited the KVS application to address fundamental software bottlenecks. Unlike prior research focusing on hardware or software in isolation, the authors aimed to full-stack (software through hardware) architect high-performance and efficient KVS platforms. Their full-system characterization identifies the critical hardware/software ingredients for high-performance KVS systems and suggests optimizations to achieve record-setting performance and energy efficiency: 120~167 million requests per second (RPS) on a single commodity server. They propose a future many-core platform and via detailed simulations demonstrate the capability of achieving a billion RPS with a single server platform.</P>
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Formant-Based Robust Voice Activity Detection
In-Chul Yoo,Hyeontaek Lim,Dongsuk Yook IEEE 2015 IEEE/ACM transactions on audio, speech, and langua Vol.23 No.12
<P>Voice activity detection (VAD) can be used to distinguish human speech from other sounds, and various applications can benefit from VAD-including speech coding and speech recognition. To accurately detect voice activity, the algorithm must take into account the characteristic features of human speech and/or background noise. In many real-life applications, noise frequently occurs in an unexpected manner, and in such situations, it is difficult to determine the characteristics of noise with sufficient accuracy. As a result, robust VAD algorithms that depend less on making correct noise estimates are desirable for real-life applications. Formants are the major spectral peaks of the human voice, and these are highly useful to distinguish vowel sounds. The characteristics of the spectral peaks are such that, these peaks are likely to survive in a signal after severe corruption by noise, and so formants are attractive features for voice activity detection under low signal-to-noise ratio (SNR) conditions. However, it is difficult to accurately extract formants from noisy signals when background noise introduces unrelated spectral peaks. Therefore, this paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise. The proposed method achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions. The proposed method is robust against various types of noise and produces a light computational load, so it is suitable for use in various applications.</P>
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