Convolutional Restricted Boltzmann Machine Research Articles

Unsupervised modulation filter learning for noise-robust speech recognition

The modulation filtering approach to robust automatic speech recognition (ASR) is based on enhancing perceptually relevant regions of the modulation spectrum while suppressing the regions susceptible to noise. In this paper, a data-driven unsupervised modulation filter learning scheme is proposed using convolutional restricted Boltzmann machine. The initial filter is learned using the speech spectrogram while subsequent filters are learned using residual spectrograms. The modulation filtered spectrograms are used for ASR experiments on noisy and reverberant speech where these features provide significant improvements over other robust features. Furthermore, the application of the proposed method for semi-supervised learning is investigated.

Auditory feature representation using convolutional restricted Boltzmann machine and Teager energy operator for speech recognition.

In this letter, authors propose an auditory feature representation technique with the filterbank learned using an annealing dropout convolutional restricted Boltzmann machine (ConvRBM) and noise-robust energy estimation using the Teager energy operator (TEO). TEO is applied on each subband of ConvRBM filterbank and pooled later to get the short-term spectral features. Experiments on AURORA 4 database show that the proposed features perform better than the Mel filterbank features. The relative improvement of 2.59%-11.63% and 1.26%-6.87% in word error rate is achieved using the time delay neural network and the bidirectional long short-term memory models, respectively.

Novel Unsupervised Auditory Filterbank Learning Using Convolutional RBM for Speech Recognition

To learn auditory filterbanks, recently, we have proposed an unsupervised learning model based on convolutional restricted Boltzmann machine RBM with rectified linear units. In this paper, theory, training algorithm of our proposed model, and detailed analysis of learned filterbank are being presented. Learning of the model with different databases shows that the model is able to learn cochlear-like impulse responses that are localized in frequency-domain. An auditory-like scale obtained from filterbanks learned from clean and noisy datasets resembles the Mel scale, which is known to mimic perceptually relevant aspect of speech. We have experimented with both cepstral denoted as ConvRBM-CC as well as filterbank features denoted as ConvRBM-BANK. On large vocabulary continuous speech recognition task, we achieved relative improvement of 7.21-17.8% in word error rate WER compared to Mel frequency cepstral coefficient MFCC features and 1.35-6.82% compared to Mel filterbank FBANK features. On AURORA 4 multicondition training database, the relative improvement in WER by 4.8-13.65% was achieved using a Hybrid Deep Neural Network-Hidden Markov Model DNN-HMM system with ConvRBM-CC features. Using ConvRBM-BANK features, we achieve absolute reduction of 1.25-3.85% in WER on AURORA 4 test sets compared to FBANK features. A context-dependent DNN-HMM system further improves performance with a relative improvement of 3.6-4.6% on an average for bigram 5k and tri-gram 5k language models. Hence, our proposed learned filterbank performs better than traditional MFCC and Mel-filterbank features for both clean and multicondition automatic speech recognition ASR tasks. A system combination of ConvRBM-BANK and FBANK features further improve performance in all ASR tasks. Cross-domain experiments where subband filters trained on one database are used for the ASR task of another database show that model learns generalized representations of speech signals.

Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine.

Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors.

A novel feature extraction method for scene recognition based on Centered Convolutional Restricted Boltzmann Machines

Scene recognition is an important research topic in computer vision, while feature extraction is a key step of scene recognition. Although classical Restricted Boltzmann Machines (RBM) can efficiently represent complicated data, it is hard to handle large images due to its complexity in computation. In this paper, a novel feature extraction method, named Centered Convolutional Restricted Boltzmann Machines (CCRBM), is proposed for scene recognition. The proposed model improves the Convolutional Restricted Boltzmann Machines (CRBM) by introducing centered factors in its learning strategy to reduce the source of instabilities. First, the visible units of the network are redefined using centered factors. Then, the hidden units are learned with a modified energy function by utilizing a distribution function, and the visible units are reconstructed using the learned hidden units. In order to achieve better generative ability, the Centered Convolutional Deep Belief Networks (CCDBN) is trained in a greedy layer-wise way. Finally, a softmax regression is incorporated for scene recognition. Extensive experimental evaluations on the datasets of natural scenes, MIT-indoor scenes, MIT-Places 205, SUN 397, Caltech 101, CIFAR-10, and NORB show that the proposed approach performs better than its counterparts in terms of stability, generalization, and discrimination. The CCDBN model is more suitable for natural scene image recognition by virtue of convolutional property.

Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes.

Discriminative features of 3-D meshes are significant to many 3-D shape analysis tasks. However, handcrafted descriptors and traditional unsupervised 3-D feature learning methods suffer from several significant weaknesses: 1) the extensive human intervention is involved; 2) the local and global structure information of 3-D meshes cannot be preserved, which is in fact an important source of discriminability; 3) the irregular vertex topology and arbitrary resolution of 3-D meshes do not allow the direct application of the popular deep learning models; 4) the orientation is ambiguous on the mesh surface; and 5) the effect of rigid and nonrigid transformations on 3-D meshes cannot be eliminated. As a remedy, we propose a deep learning model with a novel irregular model structure, called mesh convolutional restricted Boltzmann machines (MCRBMs). MCRBM aims to simultaneously learn structure-preserving local and global features from a novel raw representation, local function energy distribution. In addition, multiple MCRBMs can be stacked into a deeper model, called mesh convolutional deep belief networks (MCDBNs). MCDBN employs a novel local structure preserving convolution (LSPC) strategy to convolve the geometry and the local structure learned by the lower MCRBM to the upper MCRBM. LSPC facilitates resolving the challenging issue of the orientation ambiguity on the mesh surface in MCDBN. Experiments using the proposed MCRBM and MCDBN were conducted on three common aspects: global shape retrieval, partial shape retrieval, and shape correspondence. Results show that the features learned by the proposed methods outperform the other state-of-the-art 3-D shape features.

Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines.

The choice of features greatly influences the performance of a tissue classification system. Despite this, many systems are built with standard, predefined filter banks that are not optimized for that particular application. Representation learning methods such as restricted Boltzmann machines may outperform these standard filter banks because they learn a feature description directly from the training data. Like many other representation learning methods, restricted Boltzmann machines are unsupervised and are trained with a generative learning objective; this allows them to learn representations from unlabeled data, but does not necessarily produce features that are optimal for classification. In this paper we propose the convolutional classification restricted Boltzmann machine, which combines a generative and a discriminative learning objective. This allows it to learn filters that are good both for describing the training data and for classification. We present experiments with feature learning for lung texture classification and airway detection in CT images. In both applications, a combination of learning objectives outperformed purely discriminative or generative learning, increasing, for instance, the lung tissue classification accuracy by 1 to 8 percentage points. This shows that discriminative learning can help an otherwise unsupervised feature learner to learn filters that are optimized for classification.

Active semi-supervised learning method with hybrid deep belief networks.

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Convolutional Deep Networks for Visual Data Classification

This paper develops a semi-supervised learning algorithm called convolutional deep networks (CDN), to address the image classification problem with deep learning. First, we construct the previous several hidden layers using convolutional restricted Boltzmann machines, which can reduce the dimension and abstract the information of the images effectively. Second, we construct the following hidden layers using restricted Boltzmann machines, which can abstract the information of images quickly. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. CDN can reduce the dimension and abstract the information of the images at the same time efficiently. More importantly, the abstraction and classification procedure of CDN use the same deep architecture to optimize the same parameter in different steps continuously, which can improve the learning ability effectively. We did several experiments on two standard image datasets, and show that CDN are competitive with both representative semi-supervised classifiers and existing deep learning techniques.