Speech emotion recognition using deep 1D & 2D CNN LSTM networks
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Demircan, S., & Kahramanlı, H. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2712-y · Cited by 51 · see citing works
RetractedHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909 · see citing works
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691 · see citing works
Learning to Forget: Continual Prediction with LSTM
Gers, F. A., Schmidhuber, J., & Cummins, F. (2000) · Neural Computation · doi:10.1162/089976600300015015 · Cited by 5,869 · see citing works
The mythos of model interpretability
Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513 · see citing works
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340 · see citing works
(2011) · Cited by 3,939 · see citing works
IEMOCAP: Interactive emotional dyadic motion capture database
Busso, C., Bulut, M., Lee, C.-C., Kazemzadeh, A., Mower, E., Kim, S., Chang, J. N., Lee, S., & Narayanan, S. S. (2008) · Language Resources and Evaluation · doi:10.1007/s10579-008-9076-6 · Cited by 3,869 · see citing works
Multi-column deep neural networks for image classification
Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248110 · Cited by 3,760 · see citing works
librosa: Audio and Music Signal Analysis in Python
McFee, B., Raffel, C., Liang, D., Ellis, D. P. W., McVicar, M., Battenberg, E., & Nieto, O. (2015) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-7b98e3ed-003 · Cited by 3,099 · see citing works
Convergence Results for Neural Networks via Electrodynamics
Panigrahy, R., Rahimi, A., Sachdeva, S., et al. (2018) · Leibniz international proceedings in informatics · doi:10.4230/lipics.itcs.2018.22 · Cited by 2,848 · see citing works
A database of German emotional speech
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-446 · Cited by 2,365 · see citing works
Survey on speech emotion recognition: Features, classification schemes, and databases
Ayadi, M. M. H. E., Kamel, M. S., & Karray, F. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.020 · Cited by 2,089 · see citing works
Hybrid speech recognition with Deep Bidirectional LSTM
Graves, A., Jaitly, N., & Mohamed, A. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707742 · Cited by 1,790 · see citing works
Can we open the black box of AI?
Castelvecchi, D. (2016) · Nature · doi:10.1038/538020a · Cited by 1,773 · see citing works
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Katz, G., Barrett, C., Dill, D. L., Julian, K., & Kochenderfer, M. J. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-63387-9_5 · Cited by 1,706 · see citing works
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487629 · Cited by 1,359 · see citing works
Model selection for ecologists: the worldviews of AIC and BIC
Aho, K., Derryberry, D., & Peterson, T. (2014) · Ecology · doi:10.1890/13-1452.1 · Cited by 1,165 · see citing works
(2009) · neural information processing systems · Cited by 962 · see citing works
Speech emotion recognition using deep neural network and extreme learning machine
Han, K., Yu, D., & Tashev, I. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-57 · Cited by 824 · see citing works
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
Bergstra, J., Yamins, D., & Cox, D. (2013) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-8b375195-003 · Cited by 784 · see citing works
Solving Ill-Conditioned and Singular Linear Systems: A Tutorial on Regularization
Neumaier, A. (1998) · SIAM Review · doi:10.1137/s0036144597321909 · Cited by 646 · see citing works
Mao, Q., Dong, M., Huang, Z., & Zhan, Y. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2360798 · Cited by 568 · see citing works
Features and classifiers for emotion recognition from speech: a survey from 2000 to 2011
Anagnostopoulos, C.-N., Iliou, T., & Giannoukos, I. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-012-9368-5 · Cited by 428 · see citing works
Speech Emotion Recognition Using CNN
Huang, Z.-W., Dong, M., Mao, Q., & Zhan, Y. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654984 · Cited by 416 · see citing works
Deep learning for robust feature generation in audiovisual emotion recognition
Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401 · see citing works
Automatic speech emotion recognition using modulation spectral features
Wu, S., Falk, T. H., & Chan, W.-Y. (2011) · Speech Communication · doi:10.1016/j.specom.2010.08.013 · Cited by 389 · see citing works
Bi-modal emotion recognition from expressive face and body gestures
Gunes, H., & Piccardi, M. (2007) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2006.09.007 · Cited by 382 · see citing works
Speech Emotion Recognition Using Fourier Parameters
Wang, K., An, N., Li, B.-N., Zhang, Y.-Y., & Li, L. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2392101 · Cited by 381 · see citing works
Deep neural networks for acoustic emotion recognition: Raising the benchmarks
Stuhlsatz, A., Meyer, C., Eyben, F., Zielke, T., Meier, G., & Schuller, B. (2011) · 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2011.5947651 · Cited by 288 · see citing works
Speech emotion recognition: Features and classification models
Chen, L., Mao, X., Xue, Y., & Cheng, L. L. (2012) · Digital Signal Processing · doi:10.1016/j.dsp.2012.05.007 · Cited by 239 · see citing works
Self-citationRecognizing Emotions Induced by Affective Sounds through Heart Rate Variability
Nardelli, M., Valenza, G., Greco, A., Lanatà, A., & Scilingo, E. P. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2432810 · Cited by 219 · see citing works
Palaz, D., Collobert, R., & Magimai.-Doss, M. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-438 · Cited by 193 · see citing works
Methods for Studying Coincidences
Diaconis, P. W., & Mosteller, F. C. (1989) · Journal of the American Statistical Association · doi:10.2307/2290058 · Cited by 190 · see citing works
An experimental study of speech emotion recognition based on deep convolutional neural networks
Zheng, W. Q., Yu, J. S., & Zou, Y. X. (2015) · 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2015.7344669 · Cited by 187 · see citing works
Methods for Studying Coincidences
Diaconis, P. W., & Mosteller, F. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478847 · Cited by 173 · see citing works
Learned vs. Hand-Crafted Features for Pedestrian Gender Recognition
Antipov, G., Berrani, S.-A., Ruchaud, N., & Dugelay, J. (2015) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2733373.2806332 · Cited by 129 · see citing works
Cooperative Learning and its Application to Emotion Recognition from Speech
Zhang, Z., Coutinho, E., Deng, J., & Schuller, B. W. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2375558 · Cited by 121 · see citing works
Emotion recognition from spontaneous speech using Hidden Markov models with deep belief networks
Le, D., & Provost, E. M. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707732 · Cited by 120 · see citing works
SVM Scheme for Speech Emotion Recognition using MFCC Feature
Milton, A., Roy, S., & Selvi, S. T. (2013) · International Journal of Computer Applications · doi:10.5120/11872-7667 · Cited by 118 · see citing works
Weighted spectral features based on local Hu moments for speech emotion recognition
Sun, Y., Wen, G., & Wang, J. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.10.008 · Cited by 107 · see citing works
Zheng, B. S., Khairunizam, W., Murugappan, M., Ibrahim, N. M., Yuvaraj, R., & Mohamad, K. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.03.016 · Cited by 91 · see citing works
Yuvaraj, R., Murugappan, M., Ibrahim, N. M., Sundaraj, K., Omar, M. I., Mohamad, K., & Palaniappan, R. (2014) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.07.005 · Cited by 86 · see citing works
Emotion recognition from Assamese speeches using MFCC features and GMM classifier
Kandali, A. B., Routray, A., & Basu, T. K. (2008) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2008.4766487 · Cited by 82 · see citing works
He, L. T., Lech, M., Maddage, N. C., & Allen, N. B. (2010) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2010.11.001 · Cited by 68 · see citing works
Feature Extraction from Speech Data for Emotion Recognition
Demircan, S., & Kahramanlı, H. (2014) · Journal of Advances in Computer Networks · doi:10.7763/jacn.2014.v2.76 · Cited by 57 · see citing works
Music emotion recognition: The combined evidence of MFCC and residual phase
Nalini, N. J., & Palanivel, S. (2015) · Egyptian Informatics Journal · doi:10.1016/j.eij.2015.05.004 · Cited by 55 · see citing works
Discovering hierarchical speech features using convolutional non-negative matrix factorization
Behnke, S. (2004) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2003.1224004 · Cited by 46 · see citing works
Automatic analysis of speech F0 contour for the characterization of mood changes in bipolar patients
Guidi, A., Vanello, N., Bertschy, G., Gentili, C., Landini, L., & Scilingo, E. P. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.10.011 · Cited by 43 · see citing works
Pérez-Espinosa, H., Reyes-Garcia, C. A., & Villaseñor-Pineda, L. (2011) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.02.008 · Cited by 37 · see citing works
Huang, Y., Wu, A., Zhang, G., & Li, Y. (2015) · IET Signal Processing · doi:10.1049/iet-spr.2013.0446 · Cited by 35 · see citing works
Feature Learning In Dynamic Environments: Modeling The Acoustic Structure Of Musical Emotion.
Schmidt, E. M., Scott, J. J., & Kim, Y. E. (2012) · doi:10.5281/zenodo.1414845 · Cited by 29 · see citing works
Acoustic Emotion Recognition Using Linear and Nonlinear Cepstral Coefficients
Chenchah, F., & Lachiri, Z. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.061119 · Cited by 26 · see citing works
An analog neural network processor and its application to high-speed character recognition
Boser, B. E., Säckinger, E., Bromley, J. M., LeCun, Y., Howard, R. E., & Jackel, L. D. (2002) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1991.155214 · Cited by 21 · see citing works
Emotion Recognition System from Artificial Marathi Speech using MFCC and LDA Techniques
Waghmare, V. B., Deshmukh, R. R., Shrishrimal, P. P., et al. (2014) · Cited by 18 · see citing works
Text-Independent Phoneme Segmentation Combining EGG and Speech Data
Chen, L., Mao, X., & Yan, H. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2533865 · Cited by 18 · see citing works
Self-citationOvertraining in back‐propagation neural networks: A CRT color calibration example
Alman, D. H., & Liao, N. (2002) · Color Research & Application · doi:10.1002/col.10027 · Cited by 13 · see citing works
Speech Emotion Recognition Using Residual Phase and MFCC Features
Palanivel, S., & Balasubramanian, M. (2014) · Cited by 8 · see citing works
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Wang, J., Xue, M., Culhane, R., Diao, E., Ding, J., & Tarokh, V. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054629 · Cited by 162 · see citing works
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- Authors
- Zhao, J., Mao, X., & Chen, L.
- Affiliations
- Inner Mongolia University of Science and Technology (CN)
- Beihang University (CN)
- Corresponding author
- Chen, Lijiang
- Venue
- Biomedical Signal Processing and Control
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 47
- Pages
- 312-323
- ISSN
- 1746-8094 (ISSN-L)
- OpenAlex
- W2889717020
- Cited by
- 1,044
- References
- 64
- Citations by year
- 2026: 78 · 2025: 132 · 2024: 180 · 2023: 191 · 2022: 185 · 2021: 141 · 2020: 108 · 2019: 29
- FWCI
- 91.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biomedical Signal Processing and Control (publishedVersion)
- Funders
- National Natural Science Foundation of China (61603013)
- Fundamental Research Funds for the Central Universities (YWF-18-BJ-Y-181)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- speech emotion recognition, 1D-CNN-LSTM, log-mel spectrogram, long-range dependencies, IEMOCAP
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