As a variant of Finite mixture model (FMM), finite Inverted Dirichlet mixture model (IDMM)
can not avoid the conventional challenges, such as how to select the appropriate number of …

Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed
using gaussian mixture models, but when the time series are standardized such that the …

Vector quantisation (VQ) has been used in signal processing applications, such as
steganography, watermarking and signal compression. The performance of signal …

Finite beta mixture model (BMM) has been shown to be very flexible and powerful for
bounded support data modeling. However, BMM cannot automatically select the proper …

In this paper, we focus on a variational Bayesian learning approach to infinite Dirichlet
mixture model (VarInDMM) which inherits the confirmed effectiveness of modeling …

The performance of signal processing systems based on vector quantization depends on
codebook design. In the image compression scenario, the quality of the reconstructed …

Emotion is a conscious mental reaction accompanied by physiological and behavior
changes in human body. In speaker authentication system, emotional state of the speaker …

A novel text-independent speaker identification (SI) method is proposed. This method uses
the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic information among …

Spherical data is distributed on the sphere. The data appears in various fields such as
meteorology, biology, and natural language processing. However, a method for analysis of …

In this paper, we continue our previous work on the Dirichlet mixture model (DMM)-based
VQ to derive the performance bound of the LSF VQ. The LSF parameters are transformed …