TY - GEN

T1 - Fast and effective model order selection method to determine the number of sources in a linear transformation model

AU - Cong, Fengyu

AU - Nandi, Asoke K.

AU - He, Zhaoshui

AU - Cichocki, Andrzej

AU - Ristaniemi, Tapani

PY - 2012

Y1 - 2012

N2 - This paper formally introduces the method named as RAE (ratio of adjacent eigenvalues) for model order selection, and proposes a new approach combining the recently developed SORTE (Second ORder sTatistic of the Eigenvalues) and RAE in the context for determining the number of sources in a linear transformation model. The underlying rationale for the combination discovered through sufficient simulations is that SORTE overestimated the true order in the model and RAE underestimated the true order when the signal to noise ratio (SNR) was low. Simulations further showed that after the new method, called RAESORTE, was optimized, the true number of sources was almost correctly estimated even when the SNR was 10 dB, which is extremely difficult for any other model order selection methods; moreover, RAE took much less time than SORTE known as computational efficiency. Hence, RAE and RAESORTE appear promising for the real-time and real world signal processing.

AB - This paper formally introduces the method named as RAE (ratio of adjacent eigenvalues) for model order selection, and proposes a new approach combining the recently developed SORTE (Second ORder sTatistic of the Eigenvalues) and RAE in the context for determining the number of sources in a linear transformation model. The underlying rationale for the combination discovered through sufficient simulations is that SORTE overestimated the true order in the model and RAE underestimated the true order when the signal to noise ratio (SNR) was low. Simulations further showed that after the new method, called RAESORTE, was optimized, the true number of sources was almost correctly estimated even when the SNR was 10 dB, which is extremely difficult for any other model order selection methods; moreover, RAE took much less time than SORTE known as computational efficiency. Hence, RAE and RAESORTE appear promising for the real-time and real world signal processing.

KW - Linear transformation model

KW - model order selection

KW - number of sources

KW - ratio of adjacent eigenvalues

KW - signal to noise ratio

UR - http://www.scopus.com/inward/record.url?scp=84869852579&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84869852579

SN - 9781467310680

T3 - European Signal Processing Conference

SP - 1870

EP - 1874

BT - Proceedings of the 20th European Signal Processing Conference, EUSIPCO 2012

T2 - 20th European Signal Processing Conference, EUSIPCO 2012

Y2 - 27 August 2012 through 31 August 2012

ER -