Author image not provided
 Jiqing Han

Authors:
Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article1.00
Citation Count16
Publication count16
Publication years2005-2017
Available for download1
Average downloads per article185.00
Downloads (cumulative)185
Downloads (12 Months)27
Downloads (6 Weeks)3
SEARCH
ROLE
Arrow RightAuthor only


AUTHOR'S COLLEAGUES
See all colleagues of this author

SUBJECT AREAS
See all subject areas




BOOKMARK & SHARE


16 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 16 of 16
Sort by:

1
October 2017 Expert Systems with Applications: An International Journal: Volume 84 Issue C, October 2017
Publisher: Pergamon Press, Inc.
Bibliometrics:
Citation Count: 0

First, the spectrograms of heart cycles are scaled for comparison.Second, tensor decomposition is utilized to the scaled spectrograms.Third, the intrinsic structure information of scaled spectrograms is extracted.Fourth, more useful physiological and pathological information is reserved.Fifth, the extracted features are more discriminative. Heart sound signal analysis is an effective and convenient ...
Keywords: Scaled spectrogram, Heart sound, Tensor decomposition

2
November 2016 IEEE Transactions on Signal Processing: Volume 64 Issue 22, November 2016
Publisher: IEEE Press
Bibliometrics:
Citation Count: 0

In this paper, we focus on hidden period identification and the periodic decomposition of signals. Based on recent results on the Ramanujan subspace, we reveal the conjugate symmetry of the Ramanujan subspace with a set of complex exponential basis functions and represent the subspace as the union of a series ...

3
July 2016 Future Generation Computer Systems: Volume 60 Issue C, July 2016
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 0

Heart sound classification, used for the automatic heart sound auscultation and cardiac monitoring, plays an important role in primary health center and home care. However, one of the most difficult problems for the task of heart sound classification is the heart sound segmentation, especially for classifying a wide range of ...
Keywords: Diffusion map, Feature fusion, Autocorrelation feature, Heart sound classification

4
January 2016 Neurocomputing: Volume 173 Issue P3, January 2016
Publisher: Elsevier Science Publishers B. V.
Bibliometrics:
Citation Count: 2

As a promising technique, sparse coding has been widely used for the analysis, representation, compression, denoising and separation of speech. This technique needs a good dictionary which contains atoms to represent speech signals. Although many methods have been proposed to learn such a dictionary, there are still two problems. First, ...
Keywords: Sparse coding, Dictionary optimization, Speech denoising, Speech recognition

5
October 2015 Neural Processing Letters: Volume 42 Issue 2, October 2015
Publisher: Kluwer Academic Publishers
Bibliometrics:
Citation Count: 0

We propose an algorithm to do audio signal classification based on low-rank matrix representative audio data. Conventionally, the low-rank matrix data can be represented by a vector in high dimensional space. Some learning algorithms are then applied in such a vector space for matrix data classification. Particularly, maximum margin classifiers, ...
Keywords: Audio classification, Maximum margin, Trace norm regularization, Low-rank feature

6
August 2015 Digital Signal Processing: Volume 43 Issue C, August 2015
Publisher: Academic Press, Inc.
Bibliometrics:
Citation Count: 0

Recently, a trend in speech recognition is to introduce sparse coding for noise robustness. Although several methods have been proposed, the performance of sparse coding in speech denoising is not so optimistic. One assumption with sparse coding is that the representation of speech over the speech dictionary is sparse, while ...
Keywords: Speech denoising, Sparse coding, Basis pursuit denoising, Residual noise

7
July 2015 Information Sciences: an International Journal: Volume 310 Issue C, July 2015
Publisher: Elsevier Science Inc.
Bibliometrics:
Citation Count: 3

Consider the relationship between reconstruction error and sparseness degree and define comparable measures with more detailed information.Define measures at different angles, allowing for evaluation in different tasks. Our measures cover the representation, reconstruction, denoising and separation of speech.Put forward the optimization problem of a given dictionary and solve this problem ...
Keywords: Sparse coding, Dictionary evaluation, Dictionary optimization, Speech denoising, Speech recognition

8
September 2014 Digital Signal Processing: Volume 32, September, 2014
Publisher: Academic Press, Inc.
Bibliometrics:
Citation Count: 1

Channel distortion is one of the major factors which degrade the performances of automatic speech recognition (ASR) systems. Current compensation methods are generally based on the assumption that the channel distortion is a constant or slowly varying bias in an utterance or globally. However, this assumption is not sustained in ...
Keywords: Expectation-maximization, Channel distortion, Spectrum missing, Automatic speech recognition

9
July 2014 Circuits, Systems, and Signal Processing: Volume 33 Issue 7, July 2014
Publisher: Birkhauser Boston Inc.
Bibliometrics:
Citation Count: 0

Traditionally, most of voice activity detection (VAD) methods are based on speech features such as spectrum, temporal energy, and periodicity. The robustness of these features plays a critical role on the performance of VAD. However, since these features are always directly generated from observed signal, the robustness of these features ...
Keywords: Optimization algorithm, Learned dictionary, Mutual coherence, Sparse representation, Voice activity detection (VAD)

10 published by ACM
January 2014 ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining: Volume 5 Issue 1, December 2013
Publisher: ACM
Bibliometrics:
Citation Count: 3
Downloads (6 Weeks): 3,   Downloads (12 Months): 27,   Downloads (Overall): 185

Full text available: PDFPDF
In this article, a novel framework based on trace norm minimization for audio classification is proposed. In this framework, both the feature extraction and classification are obtained by solving corresponding convex optimization problem with trace norm regularization. For feature extraction, robust principle component analysis (robust PCA) via minimization a combination ...
Keywords: robust principle component analysis, Audio classification, online learning, proximal gradient, matrix classification, trace norm minimization, low-rank matrix

11
March 2013 IEEE Transactions on Audio, Speech, and Language Processing: Volume 21 Issue 3, March 2013
Publisher: IEEE Press
Bibliometrics:
Citation Count: 0

In this paper, we generalize the Gaussian Mixture Model (GMM) in two ways: a) by introducing novel distance measures between two vectors based on nonlinear maps to give more general mixture models; b) by building mixture models based on multiple different kinds of distributions. These two generalizations cope with different ...

12
January 2013 IEEE Transactions on Audio, Speech, and Language Processing: Volume 21 Issue 1, January 2013
Publisher: IEEE Press
Bibliometrics:
Citation Count: 0

In order to naturally combine audio information from different dimensions and build robust audio processing system, a novel framework based on low-rank tensor representation features for audio segment classification is proposed in this paper. The audio signal is first transformed into tensor format data, and then these tensor data are ...

13
November 2011 ICONIP'11: Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 2

In this paper, a novel framework based on trace norm minimization for audio event detection is proposed. In the framework, both the feature extraction and pattern classifier are made by solving corresponding convex optimization problem with trace norm regularization or under trace norm constraint. For feature extraction, robust principle component ...
Keywords: low-rank matrix, audio event detection, robust principle component analysis, matrix classification, trace norm minimization

14
December 2009 ROBIO'09: Proceedings of the 2009 international conference on Robotics and biomimetics
Publisher: IEEE Press
Bibliometrics:
Citation Count: 0

We investigate the effect of audio coding on speaker identification and verification when training and testing conditions are matched and mismatched. Experiments use popular audio coding algorithms (Windows Media Audio 9.1, Advanced Audio Coding, MPEG Audio Layer III) and a speaker identification and verification system based on Gaussian mixture models. ...

15
December 2006 ISCSLP'06: Proceedings of the 5th international conference on Chinese Spoken Language Processing
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 2

Tone plays an important lexical role in spoken tonal languages like Mandarin Chinese. In this paper we propose a two-pass search strategy for improving tonal syllable recognition performance. In the first pass, instantaneous F0 information is employed along with corresponding cepstral information in a 2-stream HMM based decoding. The F0 ...
Keywords: lattice rescoring, supra-tone units, tone modeling, tonal syllable recognition

16
October 2005 ACII'05: Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 0

Both of the prosody and spectral features are important for emotional speech synthesis. Besides prosody effects, voice quality and articulation parameters are the factors that should be considered to modify in emotional speech synthetic systems. Generally, rules and filters are designed to process these parameters respectively. This paper proves that ...



The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us