Criteria for multichannel signal separation

Yellin, Daniel M.; Weinstein, E.

doi:10.1109/78.301850

Cited by 161 publications

(67 citation statements)

References 11 publications

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“…With time-domain approaches, an objective function that measures the independence of the outputs of the separation system is usually defined as a function of the impulse responses of the unmixing system. Examples of time-domain approaches are [6], [7], [11], [18]- [20], and [33]. An advantage associated with the time-domain approaches is that they usually do not suffer from the so-called unknown local permutation problem at frequency level.…”

mentioning

confidence: 99%

Blind Source Separation Based on Time-Domain Optimization of a Frequency-Domain Independence Criterion

Mei

Yin

et al. 2006

IEEE Trans. Audio Speech Lang. Process.

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confidence: 99%

Blind Source Separation Based on Time-Domain Optimization of a Frequency-Domain Independence Criterion

Mei

Yin

et al. 2006

IEEE Trans. Audio Speech Lang. Process.

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“…So, the main problem of BSS is obtaining the original waveforms of the sources when only their mixture is available [9]. Due to its broad range of potential applications, BSS has attracted a great deal of attention, resulting in numerous BSS techniques which can be classified as the time domain methods (e.g., [10,11]), the frequency domain methods (e.g., [12,13]), adaptive (recursive) methods (e.g., [14]), and nonrecursive methods (e.g., [15]). …”

Section: Eurasip Journal On Advances In Signal Processingmentioning

confidence: 99%

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

Lerga

Sucic

Boashash

2011

EURASIP J. Adv. Signal Process.

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A method for components instantaneous frequency (IF) estimation of multicomponent signals in low signal-to-noise ratio (SNR) is proposed. The method combines a new proposed modification of a blind source separation (BSS) algorithm for components separation, with the improved adaptive IF estimation procedure based on the modified sliding pairwise intersection of confidence intervals (ICI) rule. The obtained results are compared to the multicomponent signal ICI-based IF estimation method for various window types and SNRs, showing the estimation accuracy improvement in terms of the mean squared error (MSE) by up to 23%. Furthermore, the highest improvement is achieved for low SNRs values, when many of the existing methods fail. Signal Model and Problem FormulationMany signals in practice, such as those found in speech processing, biomedical applications, seismology, machine condition monitoring, radar, sonar, telecommunication, and many other applications are nonstationary [1]. Those signals can be categorized as either monocomponent or multicomponent signals, where the monocomponent signal, unlike the multicomponent one, is characterized in the timefrequency domain by a single "ridge" corresponding to an elongated region of energy concentration [1].For a real signal s(t), its analytic equivalent z(t) is defined aswhere H {s(t)} is the Hilbert transformation of s(t), a(t) is the signal instantaneous amplitude, and φ(t) is the signal instantaneous phase. The instantaneous frequency (IF) describes the variations of the signal frequency contents with time; in the case of a frequency-modulated (FM) signal, the IF represents the FM modulation law and is often referred to as simply the IF law [2,3]. The IF of the monocomponent signal z(t) is the first derivative of its instantaneous phase, that is, ω(t) = φ (t) [1]. Furthermore, the crest of the "ridge" is often used to estimate the IF of the signal z(t) as [1]whereOn the other hand, the analytical multicomponent signal x(t) can be modeled as a sum of two or more monocomponent signals (each with its own IF ω m (t))where M is the number of signal components, a m (t) is the mth component instantaneous amplitude, and φ m (t) is its instantaneous phase. When calculating the Hilbert transform of the signal s(t) in (1), the conditions of Bedrosian's theorem need to be satisfied, that is, a(t) has to be a low frequency function with the spectrum which does not overlap with the e jφ(t) spectrum [2-5].

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“…One is to achieve BSS directly in the time domain, and the other is to work in the frequency domain. In time-domain BSS, a separation network is directly applied to the observed signals to yield separated source signals, and all variables and objective functions are held in the time domain [3][4][5][6]. However, these approaches are not very effective for the cases of long mixing channels, such as those in well-known cocktail party problems, where the mixing channels may have 500-2000 taps or more if modelled by FIR filters.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices

et al. 2008

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Criteria for multichannel signal separation

Cited by 161 publications

References 11 publications

Blind Source Separation Based on Time-Domain Optimization of a Frequency-Domain Independence Criterion

Blind Source Separation Based on Time-Domain Optimization of a Frequency-Domain Independence Criterion

An Efficient Algorithm for Instantaneous Frequency Estimation of Nonstationary Multicomponent Signals in Low SNR

Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices

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