The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel mul...
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Document subjectivity analysis has become an important aspect of web text content mining. This problem is similar to traditional text categorization, thus many related classification techniques can be adapted here. Ho...
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Document subjectivity analysis has become an important aspect of web text content mining. This problem is similar to traditional text categorization, thus many related classification techniques can be adapted here. However, there is one significant difference that more language or semantic information is required for better estimating the subjectivity of a document. Therefore, in this paper, our focuses are mainly on two aspects. One is how to extract useful and meaningful language features, and the other is how to construct appropriate language models efficiently for this special task. For the first issue, we conduct a Global-Filtering and Local-Weighting strategy to select and evaluate language features in a series of n-grams with different orders and within various distance-windows. For the second issue, we adopt Maximum Entropy （MaxEnt） modeling methods to construct our language model framework. Besides the classical MaxEnt models, we have also constructed two kinds of improved models with Gaussian and exponential priors respectively. Detailed experiments given in this paper show that with well selected and weighted language features, MaxEnt models with exponential priors are significantly more suitable for the text subjectivity analysis task.
This article proposes a model combination method to enhance the discriminability of the generative model. Generative and discriminative models have different optimization objectives and have their own advantages and d...
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This article proposes a model combination method to enhance the discriminability of the generative model. Generative and discriminative models have different optimization objectives and have their own advantages and drawbacks. The method proposed in this article intends to strike a balance between the two models mentioned above. It extracts the discriminative parameter from the generative model and generates a new model based on a multi-model combination. The weight for combining is determined by the ratio of the inter-variance to the intra-variance of the classes. The higher the ratio is, the greater the weight is, and the more discriminative the model will be. Experiments on speech recognition demonstrate that the performance of the new model outperforms the model trained with the traditional generative method.
In this paper, a non-linear adaptive control method based on SO(3) for the quadrotor attitude tracking is proposed. Distinct from other control methods on Euclidean space, the controller proposed is developed on SO(3)...
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In this paper, we present a novel audio fingerprinting method based on N-grams, which can quickly identify a segment of audio even when the audio signals are seriously distorted. We make use of N peaks in spectrum to ...
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In this paper, we present a novel audio fingerprinting method based on N-grams, which can quickly identify a segment of audio even when the audio signals are seriously distorted. We make use of N peaks in spectrum to form the audio fingerprint, which accelerates the retrieval speed greatly. We take advantage of the initial robust peaks to calculate the similarity between candidates and the input audio, which improves the retrieval accuracy significantly. The effectiveness of the N-gram method was evaluated on a music database of 10,000 songs. Experimental results show that the proposed approach outperforms two state-of-the-art algorithms (Shazam and Philips Robust Hash) in both effectiveness (in terms of retrieval accuracy) and efficiency (in terms of average retrieval time).
The a priori signal-to-noise (SNR) is one of the most important parameters in the short-time spectrum estimation techniques in speech enhancement. A new and convenient algorithm to estimate the priori SNR is involved ...
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Employing pre-trained word embeddings as preliminary features in convolutional neural networks（CNN） for natural language processing（NLP） tasks has been proved to be of *** exploit this idea by taking advantage of ...
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ISBN:
(纸本)9781509012473
Employing pre-trained word embeddings as preliminary features in convolutional neural networks（CNN） for natural language processing（NLP） tasks has been proved to be of *** exploit this idea by taking advantage of different types of word embeddings at the same *** be specific,we extend CNN models to coordinate two lookup tables,which exploit semantic word embeddings and syntactic word embeddings at the same *** test our models on several review datasets and all results indicate the positive effect on sentiment *** understand the reason behind,we explore the difference of the two word embeddings and how they influence the CNN models.
In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learni...
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In speech recognition, acoustic modeling always requires tremendous transcribed samples, and the transcription becomes intensively time-consuming and costly. In order to aid this labor-intensive process, Active Learning （AL） is adopted for speech recognition, where only the most informative training samples are selected for manual annotation. In this paper, we propose a novel active learning method for Chinese acoustic modeling, the methods for initial training set selection based on Kullback-Leibler Divergence （KLD） and sample evaluation based on multi-level confusion networks are proposed and adopted in our active learning system, respectively. Our experiments show that our proposed method can achieve satisfying performances.
A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-B...
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A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-BSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports ＆ serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems （RTOS） by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance convenient. Thus it is suitable for real-time image processing and can get satisfactory effect of image recognition.
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