Most existing automatic chord recognition systems use a chromagram in front-end processing and some sort of classifier (e.g., hidden Markov model, Gaussian mixture model (GMM), support vector machine, or other template matching technique).
This marginal gain by MCE-GMM supports our early argument mentioned in Section 1 that discriminative training of the mixture weights is not a promising approach when the number of mixture components is excessively large which is common in speaker recognition tasks.
We take a statistical approach to model the behavioral patterns using Gaussian mixture models (GMM) and support vector machines.
Therefore, we sought an optimal method using Gaussian mixture models (GMM), a support vector machine, and so on.
Here, we use a DNN for environment identification because it is more effective than other classifiers such as the Gaussian mixture model (GMM) and support vector machine for audio classification [39].
Recently, Wang et al. [12] proposed a GMM supervector kernel-support vector machine (GMM-SVM) classifier which was compared with the GMM classifier as a baseline algorithm.
The estimates are reported in Table 7 and once again the estimates confirm the steepening of the PC after 2008, especially in the GMM specification, which supports the hypothesis of a strong increase in the slope of the PC in this segment (also when one considers the long-run effect).
We devised a multimedia event detection method based on Gaussian mixture model (GMM) supervectors and support vector machines.
G support,i is the GMM used to create the ith support vector Φ support,i.
This paper reports on a comparative study which investigates the effectiveness of multiple approaches operating on GMM mean supervectors, including support vector machines and various forms of regression.
All GMP staff at LSHTM for their administrative support in relation to GMM's PhD study.
