Publications

• [1] “A Deep Neural Network with Triplet Loss for Detecting Anomaly of Respiratory Sounds,” in The Proceeding of DAGA 2021, Vienna, Austria, 2021. PDF

• [2] “Deep Learning Framework Applied for Predicting Anomaly of Respiratory Sounds,” The 2021 International Symposium on Electrical and Electronics Engineering (ISEE 2021), IEEE Computer Society, HCMC, 2021. PDF

• [3] “Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Features,” arXiv preprint at https://arxiv.org/pdf/2005.12779.pdf. PDF

• [4] “CNN-Based Framework for DCASE 2020 Task 1b Challenge,” Technical Report for Task 1b, DCASE 2020. PDF

• [5] “Low-Complexity CNN-Based Framework for Acoustic Scene Classification,” Technical Report for Task 1b, DCASE 2020. PDF

• [6] “A Re-trained Model Based On Multi-kernel Convolutional Neural Network for Acoustic Scene Classification,” in The 2020 RIVF International Conference On Computing And Communication Technologies, IEEE Computer Society, HCMC, 2020. PDF

• [7] “Acoustic Scene Classification Using A Deeper Training Method for Convolution Neural Networks,” in The International Symposium on Electrical and Electronics Engineering, ISEE 2019, IEEE, HCMC, 2019 CODE PDF

• [8] “A High Performance Dynamic ASIC-Based Audio Signal Feature Extraction (MFCC),” in The International Conference on Advanced Computing and Application, ACOM 2016, IEEE, Cantho, 2016. CODE PDF

• [9] “An Efficient Hardware Architecture for Dynamic FFT Based on Radix 2,” in The 2015 National Conference on Electronics, Communications and Information Technology, ECIT-REV, Ho Chi Minh, 2015. CODE PDF