Mixed-signal real-time adaptive blind source separation

Celik, A.; Stanaćević, Milutin; Cauwenberghs, Gert

doi:10.1109/iscas.2004.1329919

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…Because of complexity of optimization algorithms most adaptive filters are digital filter that perform digital signal processing. When processing analog signal the adaptive filter is then preceded by ADC and DAC converter [8], [10].…”

Section: IImentioning

confidence: 99%

Comparative Study of Filter Performance for Separation of Singing Voice from Music Accompaniment

Burute¹,

Patil²,

Chaudhari³

et al. 2015

International Journal of Innovative Research in Electrical, Ele

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An audio signal is a representation of sound. Audio signals have frequency range 20 to 20 kHz. Audio signals may be synthesized directly. A mixture refers to the physical combination of two or more substances on which the identities and are mixed in the form to separate out. An audio signal classification system should be able to categorize different audio input formats (speech, background noise, and music). Audio signal classification system analyzes the input audio signal and describes the signal at the output. These are used to characterize both music and speech signals. The categorization can be done on the basis of pitch, music content, music tempo and rhythm. From the comparative results it is observed that the wiener filter is better for noise reduction than others. We refer SEGSNR parameter for study because of its improved filter performance. Separating singing voice from music is very useful in many applications.

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Section: IImentioning

confidence: 99%

Comparative Study of Filter Performance for Separation of Singing Voice from Music Accompaniment

Burute¹,

Patil²,

Chaudhari³

et al. 2015

International Journal of Innovative Research in Electrical, Ele

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“…The chip also outputs the gradient signals ξ 10 , ξ 01 andξ 00 , for use in separation and localization of multiple (up to three) acoustic sources [20]. The gradient output signals are presented in complementary analog format through sample-and-hold buffers.…”

Section: E Comparator Designmentioning

confidence: 99%

“…Interestingly, some insects are capable of remarkable auditory localization at dimensions a small fraction of the wavelength, owing to differential processing of sound pressure through inter-tympanal mechanical coupling [18] or inter-aural coupled neural circuits [19]. Besides its use in bearing estimation, gradient flow provides an efficient signal representation as a front-end for blind source separation using independent component analysis [20].…”

Section: Introductionmentioning

confidence: 99%

Micropower gradient flow acoustic localizer

Stanaćević

Cauwenberghs

2005

IEEE Trans. Circuits Syst. I

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Abstract-A micropower mixed-signal system-on-chip for three-dimensional localization of a broadband acoustic source is presented. Direction cosines of the source are obtained by relating spatial and temporal differentials in the acoustic traveling wave field acquired across four coplanar microphones at sub-wavelength spacing. Correlated double sampling and leastsquares adaptive cancellation of common-mode leakthrough in the switched-capacitor analog differentials boost localization accuracy at very low aperture. A second stage of mixed-signal least-squares adaptation directly produces digital estimates of the direction cosines. The 3 mm × 3 mm chip in 0.5 µm CMOS technology quantizes signal delays with 250 ns resolution at 16 kHz sampling rate, and dissipates 54 µW power from a 3 V supply. Field tests of the processor with acoustic enclosure demonstrated its utility and endurance in tracking ground and airborne vehicles. Applications include acoustic surveillance, interactive multimedia, and intelligent hearing aids.

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“…One of the advantages of normalization (14) besides improved robustness, is its amenability to current mode implementation as opposed to logistic normalization 25 which requires exponentiation of currents. The output f ij (x) is given by:…”

Section: Fdkm Sequence Decodingmentioning

confidence: 99%

<title>A miniature low-power intelligent sensor node for persistent acoustic surveillance</title>

Cauwenberghs

Andreou

West

et al. 2005

Unattended Ground Sensor Technologies and Applications VII

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The desire for persistent, long term surveillance and covertness places severe constraints on the power consumption of a sensor node. To achieve the desired endurance while minimizing the size of the node, it is imperative to use application-specific integrated circuits (ASICs) that deliver the required performance with maximal power efficiency while minimizing the amount of communication bandwidth needed. This paper reviews our ongoing effort to integrate several micropower devices for low-power wake-up detection, blind source separation and localization and pattern classification, and demonstrate the utility of the system in relevant surveillance applications. The capabilities of each module are presented in detail along with performance statistics measured during recent experiments.

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Mixed-signal real-time adaptive blind source separation

Cited by 14 publications

References 15 publications

Comparative Study of Filter Performance for Separation of Singing Voice from Music Accompaniment

Comparative Study of Filter Performance for Separation of Singing Voice from Music Accompaniment

Micropower gradient flow acoustic localizer

<title>A miniature low-power intelligent sensor node for persistent acoustic surveillance</title>

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