Search

Highlights from the article: Deep learning employs deep neural networks (DNNs), which are neural networks with more than one hidden layer, with recently developed initialization and training strategies using massive amounts of diverse data as examples. The paper provides an overview of recent approaches to deep learning as applied to a range of speech processing tasks, primarily for automatic speech recognition (ASR), but also text-to-speech and speaker, language, and emotion recognition. The authors propose integrating deep neural network (DNN)-HMM technique with the HiLAM method where the state alignment information from the HiLAM is used to discriminatively train a DNN to further improve the system performance.

This special issue of the circuits, systems and signal processing journal focuses on recent advances and improvements in leading-edge integrated machine learning algorithms and signal processing systems. Although signal processing has been studied over several decades, the computer industry is only beginning to understand how signal processing techniques can be well integrated for the development of human-machine interfaces with the advancement of machine learning algorithms. As advances in signal processing tools and machine learning algorithms are becoming more powerful in terms of functionality and communicative capabilities, their contribution to the journal of circuits, system and signal processing is becoming more significant. [Extracted from the article]

Several works report on overcoming the challenge of complex general-purpose signal processing algorithms that result in quick utilization of chip area resources (like FPGA in implementing system on chip or network on chip). However, with the advent of machine learning and deep learning algorithms, reducing this complexity and immense use of resources is a challenging task that can improve the system's performance in real time. One of the approaches is algorithm optimization, which consumes fewer resources and produces an equivalent performance. This approach may likely be accepted in less-sensitive applications, like voice or video processing. Recently, an optimized algorithm of correlation-less Wiener–Hopf-based adaptive channel equalizer is reported, in which the overall complexity of the system is reduced by employing a more compact way of weight optimization. This paper is the continuation of that work that reports the modification in the Steepest-Descent-based adaptive channel equalizer algorithm by keeping the autocorrelation matrix out of the algorithm. The proposed design is simulated and tested at equivalent spectral performance and produces comparable performance with fewer chip resources when translated on FPGA. The proposed design supports that algorithm-level optimization may be accepted extensively for the optimal hardware-based design of DSP systems. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of artificial intelligence, human activity recognition has become a research focus. The complex, dynamic and variable features of human activities lead to the relatively low accuracy of the traditional recognition algorithms. In order to solve the problem, this paper will propose a novel structure named hierarchical deep LSTM (H-LSTM) based on long short-term memory. Firstly, the original sensor data are preprocessed by smoothing and denoising; then, the feature will be selected and extracted by time–frequency-domain method. Secondly, H-LSTM is applied to the classification of these activities. Three public UCI datasets are used to conduct simulation with the realization of the automatic extraction of feature vectors and classification of outputting recognition results. Finally, the simulation results testify to the outperformance of the H-LSTM network over other deep learning algorithms. The accuracy of H-LSTM network in human activity recognition is proved to be 99.15%. [ABSTRACT FROM AUTHOR]

At present, the deep learning super-resolution (SR) method has achieved excellent results, but it also faces problems such as large models, high computational cost, a large amounts of training data, and poor interpretability. However, traditional machine learning-based methods still have room for improvement in feature extraction and model structure. This paper constructs a gradient embedding cascade forest structure on the basis of random forest and proposes a limit gradient embedding cascaded forest SR (LGECFSR) model. In feature construction, we not only adopt the first-order gradient, the second-order gradient, and other features of the image but also fuse the information of the original LR image. In addition, image blocks of different sizes are used for training, which increases the model's generalization ability. Compared with the state-of-the-art machine learning-based methods, our method achieves the best performance and the second-best computational speed. In addition, compared with some deep learning-based methods, our model has a similar reconstruction effect and the best computational speed. In detail, for some reconstruction tasks, the Multi-Adds of LGECFSR is one-tenth to one-4000th of that of some current models. However, the SR performance of LGECFSR is the same or slightly better than that of some current classical algorithms. [ABSTRACT FROM AUTHOR]

Facial expression offers an important way of detecting the affective state of a human being. It plays a major role in various fields such as the estimation of students' attention level in online education, intelligent transportation systems and interactive games. This paper proposes a facial expression recognition system in which two channels of featured images are used to represent a 3D facial scan. Features are extracted from the local binary pattern and local directional pattern using a fine-tuned pre-trained AlexNet and a shallow convolutional neural network. The feature sets are then fused together using canonical correlation analysis. The fused feature set is fed into a multi-support vector machine (mSVM) classifier to classify the expressions into seven basic categories: anger, disgust, fear, happiness, neutral, sadness and surprise. Experiments were carried out on the Bosphorus database using tenfold cross-validation with mutually exclusive training and testing samples. The results show an average accuracy of 87.69% using an mSVM classifier with a polynomial kernel and demonstrate that the system performs better by characterizing the peculiarities in facial expressions than alternative state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, a novel Hybrid Deep Ensemble (HDE) is proposed for automatic speech disfluency classification on a sparse speech dataset. Categorizations of speech disfluencies for diagnosis of speech disorders have so long relied on sophisticated deep learning models. Such a task can be accomplished by a straightforward approach with high accuracy by the proposed model which is an optimal combination of diverse machine learning and deep learning algorithms in a hierarchical arrangement which includes a deep autoencoder that yields the compressed latent features. The proposed model has shown considerable improvement in downgrading processing time overcoming the issues of cumbersome hyper-parameter tuning and huge data demand of the deep learning algorithms with high classification accuracy. Experimental results show that the proposed Hybrid Deep Ensemble has superior performance compared to the individual base learners, and the deep neural network as well. The proposed model and the baseline models were evaluated in terms of Cohen's kappa coefficient, Hamming loss, Jaccard score, F-score and classification accuracy. [ABSTRACT FROM AUTHOR]

Acoustic Scene Classification (ASC) aims to categorize real-world audio into one of the predetermined classes that identifies the recording environment of the audio. State-of-the-art ASC algorithms have excellent performance in terms of accuracy due to the emergence of deep learning algorithms. In particular, Convolutional Neural Networks (CNN) have set a new benchmark in ASC due to their promising performance. Despite the emergence of new frameworks, the interest in ASC is growing progressively with a shift of focus from enhancing accuracy to reducing model complexity. In this work, we introduce the AtResNet, a residual atrous CNN for low complexity acoustic scene classification. The AtResNet utilizes dilated convolutions and residual connections to reduce the number of model parameters. To further enhance the performance of AtResNet, we introduce a multi-scale feature representation method called multi-scale mel spectrogram (ms2). To compute the ms2, we evaluate the mel spectrogram on the wavelet subbands of the signal. We assessed AtResNet with ms2 on three benchmark datasets in ASC. The results suggest that our method significantly outperformed the CNN-based techniques in addition to a baseline system based on log mel spectrum for signal representation. AtResNet offers a 28.73% reduction in the model parameters against a baseline CNN. Furthermore, the AtResNet has a model size of 81 KB with post-training quantization of network weights. It makes AtResNet suitable for deployment in context-aware devices. [ABSTRACT FROM AUTHOR]

In the recent past years, deep-learning-based machine learning methods have demonstrated remarkable success for a wide range of learning tasks in multiple domains. They are suitable for complex classification and regression problems in applications such as computer vision, speech recognition and other pattern analysis branches. The purpose of this article is to contribute a timely review and introduction of state-of-the-art and popular discriminative DNN, CNN and RNN deep learning techniques, the basic framework and algorithms, hardware implementations, applications in speech, and the overall benefits of deep learning. [ABSTRACT FROM AUTHOR]