參考文獻 |
[1] G. Wichern, J. Xue, H. Thornburg, B. Mechtley, and A. Spanias, “Segmentation, indexing,
and retrieval for environmental and natural sounds,” IEEE Transactions on Audio,
Speech, and Language Processing, vol. 18, no. 3, pp. 688–707, Mar. 2010.
[2] R. Bardeli, “Similarity search in animal sound databases,” IEEE Transactions on Multimedia,
vol. 11, no. 1, pp. 68–76, Jan. 2009.
[3] G. Guo and S. Z. Li, “Content-based audio classification and retrieval by support vector
machines,” IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 209–215, Jan.
2003.
[4] J. Nishimura, N. Sato, and T. Kuroda, “Speech ”siglet” detection for business microscope
(concise contribution),” in IEEE International Conference on Pervasive Computing and
Communications, 2008, pp. 147–152.
[5] B. G. Ferguson and K. W. Lo, “Acoustic cueing for surveillance and security applications,”
in Sensors, and Command, Control, Communications, and Intelligence (C3I)
Technologies for Homeland Security and Homeland Defense V , 2006.
[6] G. Virone, D. Istrate, M. Vacher, N. Noury, J. F. Serignat, and J. Demongeot, “First steps
in data fusion between a multichannel audio acquisition and an information system for
home healthcare,” in IEEE Engineering in Medicine and Biology Society, 2003, pp. 1364–
1367.
[7] M. Uddin and T. Nadeem, “Energysniffer: Home energy monitoring system using smart
phones,” in International Wireless Communications and Mobile Computing Conference,
2012, pp. 159–164.
[8] J. C. Wang, H. P. Lee, J. F. Wang, and C. B. Lin, “Robust environmental sound recognition
for home automation,” IEEE Transactions on Automation Science and Engineering,
vol. 5, no. 1, pp. 25–31, Jan. 2008.
[9] P. C. Loizou, Speech Enhancement: Theory and Practice. CRC Press, 2007.
[10] S. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Transactions
on Acoustics, Speech, and Signal Processing, vol. 27, no. 2, pp. 113–120, Apr.
1979.
[11] Y. Ephraim and D. Malah, “Speech enhancement using a minimum-mean square error
short-time spectral amplitude estimator,” IEEE Transactions on Acoustics, Speech, and
Signal Processing, vol. 32, no. 6, pp. 1109–1121, Dec. 1984.
[12] ——, “Speech enhancement using a minimum mean-square error log-spectral amplitude
estimator,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 33, no. 2,
pp. 443–445, Apr. 1985.
[13] I. Cohen, “Optimal speech enhancement under signal presence uncertainty using logspectral
amplitude estimator,” IEEE Signal Processing Letters, vol. 9, no. 4, pp. 113–
116, Apr. 2002.
[14] Y. Ephraim and H. L. V. Trees, “A signal subspace approach for speech enhancement,”
in IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2,
Apr. 1993, pp. 355–358.
[15] ——, “A signal subspace approach for speech enhancement,” IEEE Transactions on
Speech and Audio Processing, vol. 3, no. 4, pp. 251–266, Jul. 1995.
[16] J. Huang and Y. Zhao, “A dct-based fast signal subspace technique for robust speech
recognition,” IEEE Transactions on Speech and Audio Processing, vol. 8, no. 6, pp. 747–
751, Nov. 2000.
[17] A. Rezayee and S. Gazor, “An adaptive klt approach for speech enhancement,” IEEE
Transactions on Speech and Audio Processing, vol. 9, no. 2, pp. 87–95, Feb. 2001.
[18] Y. Nagata, K. Mitsubori, T. Kagi, T. Fujioka, and M. Abe, “Fast implementation of
klt-based speech enhancement using vector quantization,” IEEE Transactions on Audio,
Speech, and Language Processing, vol. 14, no. 6, pp. 2086–2097, Nov. 2006.
[19] C. H. Yang and J. F. Wang, “Noise suppression based on approximate klt with wavelet
packet expansion,” in IEEE International Conference on Acoustics, Speech and Signal
Processing, 2002, pp. 565–568.
[20] R. Miyazaki, H. Saruwatari, T. Inoue, Y. Takahashi, K. Shikano, and K. Kondo, “Musicalnoise-
free speech enhancement based on optimized iterative spectral subtraction,” IEEE
Transactions on Audio, Speech, and Language Processing, vol. 20, no. 7, pp. 2080–2094,
Sep. 2012.
[21] M. Dendrinos, S. Bakamidis, and G. Carayannis, “Speech enhancement from noise: A
regenerative approach,” Speech Communication, vol. 10, no. 1, pp. 45–67, Feb. 1991.
[22] E. Candes, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” Journal
of the ACM, vol. 58, no. 3, pp. 1–37, 2011.
[23] H. B. Barlow, Underlying the Transformations Sensory Messages. Cambridge, MA: MIT
Press, 1961.
[24] N. Cho and C.-C. J. Kuo, “Sparse music representation with source-specific dictionaries
and its application to signal separation,” IEEE Transactions on Audio, Speech, and
Language Processing, vol. 19, no. 2, pp. 326–337, Feb. 2011.
[25] S. Ewert, B. Pardo, M. Mueller, and M. D. Plumbley, “Score-informed source separation
for musical audio recordings: An overview,” IEEE Signal Processing Magazine, vol. 31,
no. 3, pp. 116–124, May 2014.
[26] A. Ozerov, E. Vincent, and F. Bimbot, “A general flexible framework for the handling
of prior information in audio source separation,” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 20, no. 4, pp. 1118–1133, May 2012.
[27] P. Smaragdis, B. Raj, and M. Shashanka, “Supervised and semi-supervised separation
of sounds from single-channel mixtures,” in International Conference on Independent
Component Analysis and Signal Separation, London, UK, 2007, pp. 414–421, isbn: 3-
540-74493-2, 978-3-540-74493-1.
[28] D. Kitamura, H. Saruwatari, K. Yagi, K. Shikano, Y. Takahashi, and K. Kondo, “Robust
music signal separation based on supervised nonnegative matrix factorization with prevention
of basis sharing,” in IEEE International Symposium on Signal Processing and
Information Technology, 2013, pp. 392–397.
[29] P. S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, “Deep learning for
monaural speech separation,” in IEEE International Conference on Acoustics, Speech
and Signal Processing, 2014, pp. 1562–1566.
[30] Y. Wang, A. Narayanan, and D. Wang, “On training targets for supervised speech separation,”
IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22,
no. 12, pp. 1849–1858, Dec. 2014.
[31] Y. Xu, J. Du, L. R. Dai, and C. H. Lee, “A regression approach to speech enhancement
based on deep neural networks,” IEEE/ACM Transactions on Audio, Speech, and
Language Processing, vol. 23, no. 1, pp. 7–19, Jan. 2015.
[32] P. S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, “Joint optimization of
masks and deep recurrent neural networks for monaural source separation,” IEEE/ACM
Transactions on Audio, Speech, and Language Processing, vol. 23, no. 12, pp. 2136–2147,
Dec. 2015.
[33] G. X. Wang, C. C. Hsu, and J. T. Chien, “Discriminative deep recurrent neural networks
for monaural speech separation,” in IEEE International Conference on Acoustics, Speech
and Signal Processing, 2016, pp. 2544–2548.
[34] D. Wang and J. S. Lim, “The unimportance of phase in speech enhancement,” IEEE
Transactions on Audio, Speech, and Language Processing, vol. 30, no. 4, pp. 679–681,
Aug. 1982.
[35] K. Michiko, G. Satoru, T. Mikio, and H. Tammo, “On the significance of phase in the
short term fourier spectrum for speech intelligibility,” Journal of the Acoustical Society
of America, vol. 127, no. 3, pp. 1432–1439, Mar. 2010.
[36] S. H. Moon, B. Kim, and I. S. Lee, “Importance of phase information in speech enhancement,”
in IEEE International Conference on Complex, Intelligent and Software Intensive
Systems, 2010, pp. 770–773.
[37] K. Paliwal, K. Wójcicki, and B. Shannon, “The importance of phase in speech enhancement,”
Speech Communication, vol. 53, no. 4, pp. 465–494, Apr. 2011.
[38] T. Gerkmann, M. Krawczyk-Becker, and J. L. Roux, “Phase processing for single-channel
speech enhancement: History and recent advances,” IEEE Signal Processing Magazine,
vol. 32, no. 2, pp. 55–66, Mar. 2015.
[39] A. Temko, R. Malkin, C. Zieger, D. Macho, and C. Nadeu, “Acoustic event detection
and classification in smart-room environments: Evaluation of chil project systems,” in IV
Biennial Workshop on Speech Technology, 2006, pp. 1–6.
[40] C. Clavel, T. Ehrette, R. Gael, and C. Paris, “Event detection for an audio-based
surveillance system,” in IEEE International Conference on Multimedia and Expo, 2005,
pp. 1306–1309.
[41] M. W. Mak and S. Y. Kung, “Low-power svm classifiers for sound event classification
on mobile devices,” in IEEE International Conference on Acoustics, Speech, and Signal
Processing, 2012, pp. 1985–1988.
[42] D. Zhao, H. Ma, and L. Liu, “Event classification for living environment surveillance using
audio sensor networks,” in IEEE International Conference on Multimedia and Expo, 2010,
pp. 528–533.
[43] S. Chen, Z. P. Sun, and B. Bridge, “Automatic traffic monitoring by intelligent sound
detection,” in IEEE Conference on Intelligent Transportation System, 1997, pp. 171–176.
[44] B. Ghoraani and S. Krishnan, “Time-frequency matrix feature extraction and classification
of environmental audio signals,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 19, no. 7, pp. 2197–2209, Sep. 2011.
[45] J. Montalvao, D. Istrate, J. Boudy, and J. Mouba, “Sound event detection in remote
health care-small learning datasets and over constrained gaussian mixture models,” in
IEEE Engineering in Medicine and Biology Society, 2010, pp. 1146–1149.
[46] N. C. Phuong and T. D. Dat, “Sound classification for event detection: Application into
medical telemonitoring,” in International Conference on Computing, Management and
Telecommunications, 2013, pp. 330–333.
[47] Y. T. Peng, C. Y. Lin, M. T. Sun, and K. C. Tsai, “Healthcare audio event classification
using hidden markov models and hierarchical hidden markov models,” in IEEE
International Conference on Multimedia and Expo, 2009, pp. 1218–1221.
[48] Y. T. Peng, C. Y. Lin, and M. T. Sun, “Audio event classification using binary hierarchical
classifiers with feature selection for healthcare applications,” in IEEE International
Symposium on Circuits and Systems, 2008, pp. 3238–3241.
[49] J. C. Wang, H. P. Lee, J. F. Wang, and C. B. Lin, “Robust environmental sound recognition
for home automation,” IEEE Transactions on Automation Science and Engineering,
vol. 5, no. 1, pp. 25–31, Jan. 2008.
[50] J. C. Wang, C. H. Lin, B. W. Chen, and M. K. Tsai, “Gabor-based nonuniform scalefrequency
map for environmental sound classification in home automation,” IEEE Transactions
on Automation Science and Engineering, vol. 11, no. 2, pp. 607–613, Apr. 2014.
[51] J. C. Wang, Y. S. Lee, and C. H. Lin, “Robust environmental sound recognition with
fast noise suppression for home automation,” IEEE Transactions on Automation Science
and Engineering, vol. 12, no. 4, pp. 1235–1242, Oct. 2015.
[52] S. Sivasankaran and K. M. M. Prabhu, “Robust features for environmental sound classification,”
in IEEE International Conference on Electronics, Computing and Communication
Technologies, 2013, pp. 1–6.
[53] K. Lee, Z. Hyung, and J. Nam, “Acoustic scene classification using sparse feature learning
and event-based pooling,” in IEEE Workshop on Applications of Signal Processing to
Audio and Acoustics, 2013, pp. 1–4.
[54] S. Chu, S. Narayanan, and C.-C. J. Kuo, “Environmental sound recognition with time frequency
audio features,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 17, no. 6, pp. 1142–1158, Aug. 2009.
[55] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via
sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 31, no. 2, pp. 210–227, Feb. 2009.
[56] M. Zhang, W. Li, L. Wang, J. Wei, Z. Wu, and Q. Liao, “Sparse coding for sound
event classification,” in Asia-Pacific Signal and Information Processing Association, 2013,
pp. 1–5.
[57] T. Komatsu, Y. Senda, and R. Kondo, “Acoustic event detection based on non-negative
matrix factorization with mixtures of local dictionaries and activation aggregation,”
in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016,
pp. 2259–2263.
[58] A. Mesaros, T. Heittola, O. Dikmen, and T. Virtanen, “Sound event detection in real
life recordings using coupled matrix factorization of spectral representations and class
activity annotations,” in IEEE International Conference on Acoustics, Speech, and Signal
Processing, 2015, pp. 151–155.
[59] V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy,
“Learning from crowds,” Journal of Machine Learning Research, vol. 11, pp. 1297–1322,
Aug. 2010.
[60] Y. H. Yang and H. H. Chen, “Machine recognition of music emotion: A review,” ACM
Transactions on Intelligent system and Technology, vol. 3, no. 3, pp. 1–30, May 2012.
[61] D. Turnbull, L. Barrington, D. Torres, and G. Lanckriet, “Semantic annotation and
retrieval of music and sound effects,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 16, pp. 467–476, Feb. 2008.
[62] M. I. Mandel and D. P. W. Ellis, “Multiple-instance learning for music information
retrieval,” in International Society for Music Information Retrieval, 2008, pp. 577–582.
[63] S. Andrews, I. Tsochantaridis, and T. Hofmann, “Support vector machines for multipleinstance
learning,” in International Conference on Neural Information Processing Systems,
2002, pp. 577–584.
[64] Y. Chen, J. Bi, and J. Z. Wang, “Miles: Multiple-instance learning via embedded instance
selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28,
no. 12, pp. 1931–1947, Dec. 2006.
[65] K. Trohidis, G. Tsoumakas, G. Kalliris, and I. P. Vlahavas, “Multi-label classification
of music into emotions,” in International Society for Music Information Retrieval, 2008,
pp. 325–330.
[66] W. Bin, Z. Erheng, H. H. Derek, H. Andrew, and Y. Qiang, “Smart: Semi-supervised
music emotion recognition with social tagging,” in SIAM Conference on Data Mining,
2013, pp. 279–287.
[67] J. A. Russell, “A circumplex model of affect,” Journal of Personality and Social Psychology,
vol. 39, no. 6, pp. 1161–1178, 1980.
[68] Y. H. Yang, Y. C. Lin, Y. F. Su, and H. H. Chen, “A regression approach to music
emotion recognition,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 16, pp. 448–457, Feb. 2008.
[69] J. C. Wang, Y. H. Yang, H. M. Wang, and S. K. Jeng, “The acoustic emotion gaussians
model for emotion-based music annotation and retrieval,” in ACM international
conference on Multimedia, 2012, pp. 89–98.
[70] K. F. MacDorman, S. Ough, and C. C. Ho, “Automatic emotion prediction of song
excerpts: Index construction, algorithm design, and empirical comparison,” Journal of
New Music Research, vol. 36, no. 4, pp. 281–299, May 2007.
[71] K. Markov and T. Matsui, “Music genre and emotion recognition using gaussian processes,”
IEEE Access, vol. 2, pp. 688–697, Jun. 2014.
[72] Y. H. Yang and H. H. Chen, “Prediction of the distribution of perceived music emotions
using discrete samples,” IEEE Trans. Audio, Speech, and Language Processing, vol. 19,
no. 7, pp. 2184–2196, Sep. 2011.
[73] H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant
dictionaries,” IEEE Transactions on Information Theory, vol. 54, no. 5, pp. 2210–2219,
May 2008.
[74] D. L. Donoho, Compressed Sensing. Manuscript, 2004.
[75] E. J. Candes and T. Tao, “Decoding by linear programming,” IEEE Transactions on
Information Theory, vol. 51, no. 12, pp. 4203–4215, Dec. 2005.
[76] A. Mahalanobis and R. Muise, “Object specific image reconstruction using a compressive
sensing architecture for application in surveillance systems,” IEEE Transactions on
Aerospace and Electronic Systems, vol. 45, no. 3, pp. 1167–1180, Jul. 2009.
[77] J. Wu, F. Liu, L. C. Jiao, X. Wang, and B. Hou, “Multivariate compressive sensing
for image reconstruction in the wavelet domain: Using scale mixture models,” IEEE
Transactions on Image Processing, vol. 20, no. 12, pp. 3483–3494, Dec. 2011.
[78] C. Deng, W. Lin, B. s. Lee, and C. T. Lau, “Robust image coding based upon compressive
sensing,” IEEE Transactions on Multimedia, vol. 14, no. 2, pp. 278–290, Apr. 2012.
[79] J. Trzasko and A. Manduca, “Highly undersampled magnetic resonance image reconstruction
via homotopic l0-minimization,” IEEE Transactions on Medical Imaging, vol. 28,
no. 1, pp. 106–121, Jan. 2009.
[80] Q. F. Tan, P. G. Georgiou, and S. Narayanan, “Enhanced sparse imputation techniques
for a robust speech recognition front-end,” IEEE Transactions on Audio, Speech, and
Language Processing, vol. 19, no. 8, pp. 2418–2429, Nov. 2011.
[81] T. V. Sreenivas and W. B. Kleijn, “Compressive sensing for sparsely excited speech
signals,” in IEEE International Conference on Acoustics, Speech and Signal Processing,
2009, pp. 4125–4128.
[82] E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal
Processing Magazine, vol. 25, no. 2, pp. 21–30, Mar. 2008.
[83] E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and
inaccurate measurements,” Communications on Pure and Applied Mathematics, vol. 59,
no. 8, pp. 1207–1223, Mar. 2006.
[84] S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,”
SIAM Journal on Scientific Computing, vol. 43, no. 1, pp. 129–159, 2001.
[85] S. G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE
Transactions on Signal Processing, vol. 41, no. 12, pp. 3397–3415, Dec. 1993.
[86] Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, “Orthogonal matching pursuit: Recursive
function approximation with applications to wavelet decomposition,” in Asilomar Conference
on Signals, Systems and Computers, 1993, pp. 40–44.
[87] T. Blumensath and M. E. Davies, “Gradient pursuits,” IEEE Transactions on Signal
Processing, vol. 56, no. 6, pp. 2370–2382, Jun. 2008.
[88] J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal
matching pursuit,” IEEE Transactions on Information Theory, vol. 53, no. 12,
pp. 4655–4666, Dec. 2007.
[89] D. Wu, W. P. Zhu, and M. N. S. Swamy, “Compressive sensing-based speech enhancement
in non-sparse noisy environments,” IET Signal Processing, vol. 7, no. 5, pp. 450–457, Jul.
2013.
[90] J. F. Gemmeke, H. V. Hamme, B. Cranen, and L. Boves, “Compressive sensing for missing
data imputation in noise robust speech recognition,” IEEE Journal of Selected Topics in
Signal Processing, vol. 4, no. 2, pp. 272–287, Apr. 2010.
[91] D. Wu, W. P. Zhu, and M. N. S. Swamy, “The theory of compressive sensing matching
pursuit considering time-domain noise with application to speech enhancement,” IEEE/
ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 3, pp. 682–
696, Mar. 2014.
[92] S. Y. Low, D. S. Pham, and S. Venkatesh, “Compressive speech enhancement,” Speech
Communication, vol. 55, no. 6, pp. 757–768, Jul. 2013.
[93] T. G. Kang, K. Kwon, J. W. Shin, and N. S. Kim, “Nmf-based target source separation
using deep neural network,” IEEE Signal Processing Letters, vol. 22, no. 2, pp. 229–233,
Feb. 2015.
[94] S. Nie, S. Liang, H. Li, X. L. Zhang, Z. L. Yang, W. J. Liu, and L. K. Dong, “Exploiting
spectro-temporal structures using nmf for dnn-based supervised speech separation,” in
IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, pp. 469–
473.
[95] M. Krawczyk and T. Gerkmann, “Stft phase reconstruction in voiced speech for an improved
single-channel speech enhancement,” IEEE/ACM Transactions on Audio, Speech,
and Language Processing, vol. 22, no. 12, pp. 1931–1940, Dec. 2014.
[96] B. J. King and L. Atlas, “Single-channel source separation using complex matrix factorization,”
IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, no. 8,
pp. 2591–2597, Nov. 2011.
[97] D. S. Williamson, Y. Wang, and D. Wang, “Complex ratio masking for joint enhancement
of magnitude and phase,” in IEEE International Conference on Acoustics, Speech and
Signal Processing, 2016, pp. 5220–5224.
[98] ——, “Complex ratio masking for monaural speech separation,” IEEE/ACM Transactions
on Audio, Speech, and Language Processing, vol. 24, no. 3, pp. 483–492, Mar. 2016.
[99] A. J. R. Simpson, “Deep transform: Cocktail party source separation via complex convolution
in a deep neural network,” CoRR, vol. abs/1504.02945, Apr. 2015.
[100] L. Drude, B. Raj, and R. Haeb-Umbach, “On the appropriateness of complex-valued
neural networks for speech enhancement,” in Interspeech, 2016.
[101] K. Osako, R. Singh, and B. Raj, “Complex recurrent neural networks for denoising speech
signals,” in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics,
2015, pp. 1–5.
[102] H. Lee, A. Battle, R. Raina, and A. Y. Ng., “Efficient sparse coding algorithms,” in
Neural Information Processing Systems, 2006, pp. 801–808.
[103] K. Gregor and Y. Lecun, “Learning fast approximations of sparse coding,” in International
Conference on Machine Learning, 2005, pp. 318–326.
[104] J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Supervised dictionary learning,”
in Neural Information Processing Systems, 2008, pp. 1033–1040.
[105] Q. Zhang and B. Li, “Discriminative k-svd for dictionary learning in face recognition,”
in IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 2691–2698.
[106] H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel
dictionaries for object recognition,” IEEE Transactions on Image Processing, vol. 22,
no. 12, pp. 5123–5135, Dec. 2013.
[107] Y. Xie, W. Zhang, C. Li, S. Lin, Y. Qu, and Y. Zhang, “Discriminative object tracking via
sparse representation and online dictionary learning,” IEEE Transactions on Cybernetics,
vol. 44, no. 4, pp. 539–553, Apr. 2014.
[108] C. Lu, J. Shi, and J. Jia, “Online robust dictionary learning,” in IEEE Conference on
Computer Vision and Pattern Recognition, 2013, pp. 415–422.
[109] W. Liu, Z. Yu, M. Yang, L. Lu, and Y. Zou, “Joint kernel dictionary and classifier learning
for sparse coding via locality preserving k-svd,” in IEEE International Conference on
Multimedia and Expo, 2015, pp. 1–6.
[110] Y. S. Lee, C. Y. Wang, S. Mathulaprangsan, J. H. Zhao, and J. C. Wang, “Localitypreserving
k-svd based joint dictionary and classifier learning for object recognition,” in
ACM Multimedia, Amsterdam, The Netherlands, 2016.
[111] L. Zhang, M. Yang, and X. Feng, “Sparse representation or collaborative representation:
Which helps face recognition?” In IEEE Conference on Computer Vision and Pattern
Recognition, 2011, pp. 471–478.
[112] R. Miotto and G. Lanckriet, “A generative context model for semantic music annotation
and retrieval,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20,
pp. 1096–1108, Aug. 2012.
[113] S. Ness, A. Theocharis, G. Tzanetakis, and L. Martins, “Improving automatic music tag
annotation using stacked generalization of probabilistic svm outputs,” in ACM international
conference on Multimedia, 2009, pp. 705–708.
[114] Y. Yang, Y. Lin, A. Lee, and H. Chen, “Improving musical concept detection by ordinal
regression and context fusion,” in International Society for Music Information Retrieval,
2009, pp. 147–152.
[115] B. Wu, E. H. Zhong, A. Horner, and Q. Yang, “Music emotion recognition by multi-label
multi-layer multi-instance multi-view learning,” in ACM Multimedia, 2014, pp. 117–126.
[116] E. Mower, M. J. Mataric, and S. Narayanan, “A framework for automatic human emotion
classification using emotion profiles,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 19, pp. 1057–1070, Jul. 2011.
[117] Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei, “Hierarchical dirichlet processes,”
Journal of the American Statistical Association, vol. 101, no. 476, pp. 1566–1781, Dec.
2006.
[118] D. Kim and A. Oh, “Accounting for data dependencies within a hierarchical dirichlet
process mixture model,” in ACM international conference on Information and knowledge
management, 2011, pp. 873–878.
[119] K. Yoshii and M. Goto, “A nonparametric bayesian multipitch analyzer based on infinite
latent harmonic allocation,” IEEE Trans. Audio, Speech, and Language Processing,
vol. 20, no. 3, pp. 717–730, Mar. 2012.
[120] M. Hoffman, D. Blei, and P. Cook, “Content-based musical similarity computation using
the hierarchical dirichlet process,” in International Society for Music Information
Retrieval, 2008, pp. 349–354.
[121] M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete
dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54,
no. 11, pp. 4311–4322, Nov. 2006.
[122] M. Cooke, P. Green, L. Josifovski, and A. Vizinho, “Robust automatic speech recognition
with missing and unreliable acoustic data,” Speech Communication, vol. 34, no. 3,
pp. 267–285, Jun. 2001.
[123] L. Josifovski, M. Cooke, P. Green, and A. Vizinho, “State based imputation of missing
data for robust speech recognition and speech enhancement,” in Eurospeech, 1999,
pp. 2837–2840.
[124] J. F. Gemmeke and B. Cranen, “Using sparse representations for missing data imputation
in noise robust speech recognition,” in European Signal Processing Conference, 2008.
[125] S. Roweis, “Factorial models and refiltering for speech separation and denoising,” in
Interspeech, 2003, pp. 1009–1012.
[126] A. Varga and H. J. M. Steeneken, “Assessment for automatic speech recognition ii:
Noisex-92: A database and an experiment to study the effect of additive noise on speech
recognition systems,” Speech Communication, vol. 12, no. 3, pp. 247–251, Jul. 1993.
[127] Freesound, 2013. [Online]. Available: http://www.freesound.org.
[128] A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation
of speech quality (pesq)-a new method for speech quality assessment of telephone networks
and codecs,” in IEEE International Conference on Acoustics, Speech and Signal
Processing, 2001, pp. 749–752.
[129] G. Kim, Y. Lu, Y. Hu, and P. Loizou, “An algorithm that improves speech intelligibility
in noise for normal-hearing listeners,” J. Acoust. Soc. Amer., vol. 126, pp. 1486–1494,
2009.
[130] J. Antoni, “The spectral kurtosis: A useful tool for characterising non-stationary signals,”
Mechanical Systems and Signal Processing, vol. 20, no. 2, pp. 282–307, Feb. 2006.
[131] S. W. P. Marziliano F. Dufaux and T. Ebrahimi, “Perceptual blur and ringing metrics:
Application to jpeg2000,” Signal Processing: Image Communication, vol. 19, no. 2,
pp. 163–172, 2004.
[132] Y. Lu and P. C. Loizou, “Estimators of the magnitude-squared spectrum and methods
for incorporating snr uncertainty,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 19, no. 5, pp. 1123–1137, Jul. 2011.
[133] N. Lyubimov and M. Kotov, “Non-negative matrix factorization with linear constraints
for single-channel speech enhancement.,” in Interspeech, 2013, pp. 446–450.
[134] Y. S. Lee, C. Y. Wang, S. F. Wang, J. C. Wang, and C. H. Wu, “Fully complex deep neural
network for phase-incorporating monaural source separation,” in IEEE International
Conference on Acoustics, Speech and Signal Processing, 2017.
[135] H. Leung and S. Haykin, “The complex backpropagation algorithm,” IEEE Transactions
on Signal Processing, vol. 39, no. 9, pp. 2101–2104, Sep. 1991.
[136] N. Benvenuto and F. Piazza, “On the complex backpropagation algorithm,” IEEE Transactions
on Signal Processing, vol. 40, no. 4, pp. 967–969, Apr. 1992.
[137] R. Remmert, Theory of Complex Functions. New York, NY, USA: Springer, 1991.
[138] W. Wirtinger, “Zur formalen theorie der funktionen von mehr komplexen veranderlichen,”
Mathematische Annalen, vol. 97, pp. 357–376, 1927.
[139] D. H. Brandwood, “A complex gradient operator and its application in adaptive array
theory,” IEE Proceedings F- Communications, Radar and Signal Processing, vol. 130,
no. 1, pp. 11–16, Feb. 1983.
[140] A. Ng, “Sparse autoencoder,” CS294A Lecture Notes, Stanford Univ., CA, USA, Tech.
Rep., 2011.
[141] M. J. Ablowitz and A. S. Fokas, Complex Variables. Cambridge, U.K.: Cambridge Univ.
Press, 2003.
[142] G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural
networks for large vocabulary speech recognition,” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 20, no. 1, pp. 30–42, Jan. 2012.
[143] N. Guberman, “On complex valued convolutional neural networks,” CoRR, vol. abs/
1602.09046, Feb. 2016.
[144] J. Sohl-Dickstein, B. Poole, and S. Ganguli, “Fast large-scale optimization by unifying
stochastic gradient and quasi-newton methods,” in International Conference on International
Conference on Machine Learning, 2014, pp. 604–612.
[145] K. Kreutz-Delgado, “The complex gradient operator and the cr-calculus. univ,” Univ.
California, San Diego, Tech. Rep., 2009.
[146] C. L. Hsu and J. S. R. Jang, “On the improvement of singing voice separation for monaural
recordings using the mir-1k dataset,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 18, no. 2, pp. 310–319, Feb. 2010.
[147] E. Vincent, R. Gribonval, and C. Fevotte, “Performance measurement in blind audio
source separation,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 14, no. 4, pp. 1462–1469, Jul. 2006.
[148] P. C. Loizou, Speech Enhancement: Theory and Practice. CRC Press, 2007.
[149] A. W. Rix, J. G. Beerends, M. P. Hollier, and A. P. Hekstra, “Perceptual evaluation
of speech quality (pesq)-a new method for speech quality assessment of telephone networks
and codecs,” in IEEE International Conference on Acoustics, Speech and Signal
Processing, 2001, pp. 749–752.
[150] T. Tieleman and G. Hinton, “Lecture 6.5 - rmsprop,” COURSERA: Neural Networks for
Machine Learning, Tech. Rep., 2012.
[151] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” CoRR, vol. abs/
1412.6980, Dec. 2014.
[152] H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary
learning,” in IEEE International Conference on Acoustics, Speech, and Signal Processing,
2012, pp. 2021–2024.
[153] A. Golts and M. Elad, “Linearized kernel dictionary learning,” CoRR, vol. abs/1509.05634,
2015.
[154] F. Font, G. Roma, and X. Serra, “Freesound technical demo,” in ACM Multimedia,
Barcelona, Spain, 2013, pp. 411–412, isbn: 978-1-4503-2404-5.
[155] T. Kemp, M. Schmidt, M. Westphal, and A. Waibel, “Strategies for automatic segmentation
of audio data,” in IEEE International Conference on Acoustics, Speech, and Signal
Processing, 2000, pp. 1423–1426.
[156] M. Aharon, M. Elad, and A. Bruckstein, “K-svd: An algorithm for designing overcomplete
dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54,
no. 11, pp. 4311–4322, Nov. 2006.
[157] I. CVX Research, CVX: Matlab software for disciplined convex programming, version
2.0, http://cvxr.com/cvx, 2012.
[158] R. Rubinstein, M. Zibulevsky, and M. Elad, “Efficient implementation of the k-svd algorithm
using batch orthogonal matching pursuit,” Tech. Rep., 2008.
[159] R. Hennequin, Nmf-matlab, https://github.com/romi1502/NMF-matlab, 2015.
[160] D. J. Aldous, “Exchangeability and related topics,” Ecole d̀Été de Probabilités de Saint-
Flour XIII¡V1983, pp. 1–198, 1985.
[161] D. Blackwell and J. MacQueen, “Ferguson distributions via polya urn schemes,” Annals
of Statistics, vol. 1, no. 2, pp. 353–355, Mar. 1973.
[162] Y. W. Teh, “Dirichlet processes,” Encyclopedia of Machine Learning, 2010.
[163] C. E. Antoniak, “Mixtures of dirichlet processes with applications to bayesian nonparametric
problems,” The Annals of Statistics, vol. 2, no. 6, pp. 1152–1174, Nov. 1974.
[164] S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K. R. Müller, “Fisher discriminant
analysis with kernels,” IEEE Neural Networks for Signal Processing, pp. 41–48, Aug.
1999.
[165] K. P. Murphy, Conjugate bayesian analysis of the gaussian distribution, available: http://
www.cs.ubc.ca/ murphyk/Papers/bayesGauss.pdf, 2007.
[166] C. Fraley and A. E. Raftery, “Bayesian regularization for normal mixture estimation and
model-based clustering,” Journal of Classification, vol. 24, no. 2, pp. 155–181, Sep. 2007.
[167] K. Huang and S. Aviyente, “Sparse representation for signal classification,” in International
Conference on Neural Information Processing Systems, 2006, pp. 609–616.
[168] R. He, W. S. Zheng, B. G. Hu, and X. W. Kong, “Two-stage nonnegative sparse representation
for large-scale face recognition,” IEEE Transactions on Neural Networks and
Learning Systems, vol. 24, no. 1, pp. 35–46, Jan. 2013.
[169] M. Grant and S. Boyd, Cvx: Matlab software for disciplined convex programming, available:
http://cvxr.com/cvx, 2013.
[170] ——, “Graph implementations for nonsmooth convex programs,” in Lecture Notes in
Control and Information Sciences, 2008, pp. 95–110.
[171] Y. E. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson, J. Scott, J. A.
Speck, and D. Turnbull, “Music emotion recognition: A state of the art review,” in
International Society for Music Information Retrieval, 2010, pp. 255–266.
[172] O. Lartillot and P. Toiviainen, “A matlab toolbox for musical feature extraction from
audio,” in International Conference on Digital Audio Effects, 2007, pp. 237–244.
[173] M. D. Escobar and M. West, “Bayesian density estimation and inference using mixtures,”
Journal of the American Statistical Association, vol. 90, no. 430, pp. 577–588, Jun. 1995.
[174] G. Tsoumakas, E. Spyromitros-Xioufis, J. Vilcek, and I. Vlahavas, “Mulan: A java library
for multi-label learning,” Journal of Machine Learning Research, vol. 12, pp. 2411–2414,
Jul. 2011.
[175] L. Su, C. C. M. Yeh, J. Y. Liu, J. C. Wang, and Y. H. Yang, “A systematic evaluation
of the bag-of-frames representation for music information retrieval,” IEEE Transactions
on Multimedia, vol. 16, no. 5, pp. 1188–1200, Aug. 2014. |