Learnt dictionary based active learning method for environmental sound event tagging

[1]  R. Nedunchelian,et al.  Global statistical features-based approach for Acoustic Event Detection , 2018, Applied Acoustics.

[2]  Chung-Hsien Wu,et al.  Sound Event Recognition Using Auditory-Receptive-Field Binary Pattern and Hierarchical-Diving Deep Belief Network , 2018, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[3]  Wanting Ji,et al.  Dictionary-based active learning for sound event classification , 2018, Multimedia Tools and Applications.

[4]  Xun Wang,et al.  Review on mining data from multiple data sources , 2018, Pattern Recognit. Lett..

[5]  Xiaofeng Zhu,et al.  Efficient kNN Classification With Different Numbers of Nearest Neighbors , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Xiaofeng Zhu,et al.  Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection , 2018, IEEE Transactions on Knowledge and Data Engineering.

[7]  Huibin Wang,et al.  Polarization and solar altitude correlation analysis and application in object detection , 2017, 2017 International Conference on Progress in Informatics and Computing (PIC).

[8]  Tuomas Virtanen,et al.  Learning vocal mode classifiers from heterogeneous data sources , 2017, 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[9]  Roneel V. Sharan,et al.  Robust acoustic event classification using deep neural networks , 2017, Inf. Sci..

[10]  Tuomas Virtanen,et al.  Active learning for sound event classification by clustering unlabeled data , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  Xudong Jiang,et al.  Sound-Event Classification Using Robust Texture Features for Robot Hearing , 2017, IEEE Transactions on Multimedia.

[12]  Takumi Kobayashi,et al.  Urban sound event classification based on local and global features aggregation , 2017 .

[13]  Eduardo Coutinho,et al.  Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments , 2016, PloS one.

[14]  Jörn Anemüller,et al.  Classification of human cough signals using spectro-temporal Gabor filterbank features , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Nicolai Petkov,et al.  Audio Surveillance of Roads: A System for Detecting Anomalous Sounds , 2016, IEEE Transactions on Intelligent Transportation Systems.

[16]  Karol J. Piczak.  ESC: Dataset for Environmental Sound Classification , 2015, ACM Multimedia.

[17]  P. Roe,et al.  A survey of tagging techniques for music, speech and environmental sound , 2014, Artificial Intelligence Review.

[18]  Justin Salamon,et al.  A Dataset and Taxonomy for Urban Sound Research , 2014, ACM Multimedia.

[19]  Antonio Ortega,et al.  Active semi-supervised learning using sampling theory for graph signals , 2014, KDD.

[20]  Michael Elad,et al.  Multi-Scale Dictionary Learning Using Wavelets , 2011, IEEE Journal of Selected Topics in Signal Processing.

[21]  Tuomas Virtanen,et al.  Acoustic event detection in real life recordings , 2010, 2010 18th European Signal Processing Conference.

[22]  Shrikanth Narayanan,et al.  Environmental Sound Recognition With Time–Frequency Audio Features , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[23]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[24]  Yuguo Wang,et al.  A Tree-Based Multi-class SVM Classifier for Digital Library Document , 2008, 2008 International Conference on MultiMedia and Information Technology.

[25]  A. Fleury,et al.  Sound and speech detection and classification in a Health Smart Home , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[27]  O. Chapelle,et al.  Semi-Supervised Learning , 2006 .

[28]  Ruili Wang,et al.  Spoken affect classification using neural networks , 2005, 2005 IEEE International Conference on Granular Computing.

[29]  Ruili Wang,et al.  Real-time spoken affect classification and its application in call-centres , 2005, Third International Conference on Information Technology and Applications (ICITA'05).

[30]  Manuel Rosa-Zurera,et al.  Transient modeling by matching pursuits with a wavelet dictionary for parametric audio coding , 2004, IEEE Signal Processing Letters.

[31]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[32]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

[33]  Kjersti Engan,et al.  Method of optimal directions for frame design , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[34]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[35]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[36]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[37]  Yusuf Yaslan,et al.  Sparse coding based classifier ensembles in supervised and active learning scenarios for data classification , 2018, Expert Syst. Appl..

[38]  Tuomas Virtanen,et al.  Environmental noise monitoring using source classification in sensors , 2018 .

[39]  Kire Trivodaliev,et al.  A review of Internet of Things for smart home: Challenges and solutions , 2017 .

[40]  Jian Zhang,et al.  A Certainty-Based Active Learning Framework of Meeting Speech Summarization , 2014 .

[41]  Y. Zhang,et al.  Transductive cost-sensitive lung cancer image classification , 2012, Applied Intelligence.

[42]  Jiawei Han,et al.  K-Medoids Clustering , 2010, Encyclopedia of Machine Learning.

[43]  Philippe Thomas,et al.  Semi-Supervised Learning by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien (Review) , 2009 .