Watson Mixture Model Research Articles

A Novel Capability of Object Identification and Recognition Based on Integrated mWMM

In the last decade, there has been remarkable progress in the areas of object detection and recognition due to high-quality color images along with their depth maps provided by RGB-D cameras. They enable artificially intelligent machines to easily detect and recognize objects and make real-time decisions according to the given scenarios. Depth cues can improve the quality of object detection and recognition. The main purpose of this research study to find an optimized way of object detection and identification we propose techniques of object detection using two RGB-D datasets. The proposed methodology extracts image normally from depth maps and then performs clustering using the Modified Watson Mixture Model (mWMM). mWMM is challenging to handle when the quality of the image is not good. Hence, the proposed RGB-D-based system uses depth cues for segmentation with the help of mWMM. Then it extracts multiple features from the segmented images. The selected features are fed to the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) for detecting objects. We achieved 92.13% of mean accuracy over NYUv1 dataset and 90.00% of mean accuracy for the Redweb_v1 dataset. Finally, their results are compared and the proposed model with CNN outperforms other state-of-the-art methods. The proposed architecture can be used in autonomous cars, traffic monitoring, and sports scenes.

Target Speaker Localization Based on the Complex Watson Mixture Model and Time-Frequency Selection Neural Network

Common sound source localization algorithms focus on localizing all the active sources in the environment. While the source identities are generally unknown, retrieving the location of a speaker of interest requires extra effort. This paper addresses the problem of localizing a speaker of interest from a novel perspective by first performing time-frequency selection before localization. The speaker of interest, namely the target speaker, is assumed to be sparsely active in the signal spectra. The target speaker-dominant time-frequency regions are separated by a speaker-aware Long Short-Term Memory (LSTM) neural network, and they are sufficient to determine the Direction of Arrival (DoA) of the target speaker. Speaker-awareness is achieved by utilizing a short target utterance to adapt the hidden layer outputs of the neural network. The instantaneous DoA estimator is based on the probabilistic complex Watson Mixture Model (cWMM), and a weighted maximum likelihood estimation of the model parameters is accordingly derived. Simulative experiments show that the proposed algorithm works well in various noisy conditions and remains robust when the signal-to-noise ratio is low and when a competing speaker exists.

Axially Symmetric Data Clustering Through Dirichlet Process Mixture Models of Watson Distributions.

This paper proposes a Bayesian nonparametric framework for clustering axially symmetric data. Our approach is based on a Dirichlet processes mixture model with Watson distributions, which can also be considered as the infinite Watson mixture model. In this paper, first, we extend the finite Watson mixture model into its infinite counterpart based on the framework of truncated Dirichlet process mixture model with a stick-breaking representation. Second, we propose a coordinate ascent mean-field variational inference algorithm that can effectively learn the parameters of our model with closed-form solutions; Third, to cope with a massive data set, we develop a stochastic variational inference algorithm to learn the proposed model through the method of stochastic gradient ascent; Finally, the proposed nonparametric Bayesian model is evaluated through simulated axially symmetric data sets and a real-world application, namely, gene expression data clustering.

Online MVDR Beamformer Based on Complex Gaussian Mixture Model With Spatial Prior for Noise Robust ASR

This paper considers acoustic beamforming for noise robust automatic speech recognition. A beamformer attenuates background noise by enhancing sound components coming from a direction specified by a steering vector. Hence, accurate steering vector estimation is paramount for successful noise reduction. Recently, time-frequency masking has been proposed to estimate the steering vectors that are used for a beamformer. In particular, we have developed a new form of this approach, which uses a speech spectral model based on a complex Gaussian mixture model CGMM to estimate the time-frequency masks needed for steering vector estimation, and extended the CGMM-based beamformer to an online speech enhancement scenario. Our previous experiments showed that the proposed CGMM-based approach outperforms a recently proposed mask estimator based on a Watson mixture model and the baseline speech enhancement system of the CHiME-3 challenge. This paper provides additional experimental results for our online processing, which achieves performance comparable to that of batch processing with a suitable block-batch size. This online version reduces the CHiME-3 word error rate WER on the evaluation set from 8.37% to 8.06%. Moreover, in this paper, we introduce a probabilistic prior distribution for a spatial correlation matrix a CGMM parameter, which enables more stable steering vector estimation in the presence of interfering speakers. In practice, the performance of the proposed online beamformer degrades with observations that contain only noise or/and interference because of the failure of the CGMM parameter estimation. The introduced spatial prior enables the target speaker's parameter to avoid overfitting to noise or/and interference. Experimental results show that the spatial prior reduces the WER from 38.4% to 29.2% in a conversation recognition task compared with the CGMM-based approach without the prior, and outperforms a conventional online speech enhancement approach.

Variational Inference for Watson Mixture Model.

This paper addresses modelling data using the Watson distribution. The Watson distribution is one of the simplest distributions for analyzing axially symmetric data. This distribution has gained some attention in recent years due to its modeling capability. However, its Bayesian inference is fairly understudied due to difficulty in handling the normalization factor. Recent development of Markov chain Monte Carlo (MCMC) sampling methods can be applied for this purpose. However, these methods can be prohibitively slow for practical applications. A deterministic alternative is provided by variational methods that convert inference problems into optimization problems. In this paper, we present a variational inference for Watson mixture models. First, the variational framework is used to side-step the intractability arising from the coupling of latent states and parameters. Second, the variational free energy is further lower bounded in order to avoid intractable moment computation. The proposed approach provides a lower bound on the log marginal likelihood and retains distributional information over all parameters. Moreover, we show that it can regulate its own complexity by pruning unnecessary mixture components while avoiding over-fitting. We discuss potential applications of the modeling with Watson distributions in the problem of blind source separation, and clustering gene expression data sets.

Location Feature Integration for Clustering-Based Speech Separation in Distributed Microphone Arrays

In distributed microphone arrays (DMAs) the source location information can be defined at the intra and inter-node levels. Indeed, while the first type of information results from the diversity of acoustic channels recorded by microphones embedded in the same node, the second is attributed to the differences between the acoustic channels observed by spatially distributed nodes. Both cues are very useful in DMA processing, and the aim of this paper is to utilize both of them to cluster and separate multiple competing speech signals. To capture the intra-node information, we employ the normalized recording vector, while at the inter-node level, we consider different features including the energy level differences with and without the phase differences between nodes. We model the intra-node information using the Watson mixture model (WMM), and propose using the Gamma mixture model (GaMM), Dirichlet mixture model (DMM), and WMM to model different inter-node location features. Furthermore, we propose several integrations of the intra-node and inter-node feature contributions to cluster speech recordings using the expectation maximization algorithm. Finally, simulation results are provided to demonstrate the performance of all ensuing methods.

Towards online maximum-likelihood-based speech clustering and separation

This paper introduces an approach for online speech source clustering and separation, which is based on the utilization of the multichannel location information in a recursive expectation maximization (EM) algorithm. Specifically, the normalized multichannel speech-recording vector is employed as a feature vector and is modeled using Watson mixture model. The model parameters are determined by maximizing the data likelihood at every time-frequency slot in an online processing manner. Consequently, the proposed approach can continuously adjust the speech clusters. Promising results showing the advantage of the proposed approach over the batch EM algorithm in the case of two speakers with speaker movement are obtained.