Bibtex
publications.bib
@inproceedings{Martinsson2024,
author = {Martinsson, John and Sandsten, Maria},
booktitle = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
file = {:home/john/Downloads/20230901073815_501780_1140 (1).pdf:pdf},
keywords = {Deep learning,STFT,adaptive transforms,audio classification,learnable Mel spectrogram},
title = ,
url = {TBD},
year = {2024}
}
@article{Martinsson2022a,
abstract = {Fires are a major hazard resulting in high monetary costs, personal suffering, and irreplaceable losses. The consequences of a fire can be mitigated by early detection systems which increase the potential for successful intervention. The number of false alarms in current systems can for some applications be very high, but could be reduced by increasing the reliability of the detection system by using complementary signals from multiple sensors. The current study investigates the novel use of machine learning for fire event detection based on acoustic sensor measurements. Many materials exposed to heat give rise to acoustic emissions during heating, pyrolysis and burning phases. Further, sound is generated by the heat flow associated with the flame itself. The acoustic data collected in this study is used to define an acoustic sound event detection task, and the proposed machine learning method is trained to detect the presence of a fire event based on the emitted acoustic signal. The method is able to detect the presence of fire events from the examined material types with an overall F-score of 98.4%. The method has been developed using laboratory scale tests as a proof of concept and needs further development using realistic scenarios in the future.},
author = {Martinsson, John and Runefors, Marcus and Frantzich, H{\aa}kan and Glebe, Dag and McNamee, Margaret and Mogren, Olof},
doi = {10.1007/s10694-022-01307-1},
file = {:home/john/Downloads/s10694-022-01307-1 (1).pdf:pdf},
issn = {15728099},
journal = {Fire Technology},
keywords = {Acoustic emissions,Artificial intelligence,Deep neural networks,Fire detection,Machine learning,Sound},
pages = {3385--3403},
title = ,
url = {https://link.springer.com/article/10.1007/s10694-022-01307-1},
year = {2022}
}
@techreport{Glebe2022,
author = {Glebe, Dag and Johansson, Torbj{\"{o}}rn and Martinsson, John and Genell, Anders},
file = {:home/john/Downloads/FULLTEXT01 (1).pdf:pdf},
institution = {Naturv{\aa}rdsverket},
isbn = {9789162070861},
keywords = {Rapport 7086 – Bullerdatainsamling och autonom art},
number = {December},
pages = {Report 7086},
title = r att underl{\"{a}}tta milj{\"{o}}{\"{o}}vervakning}},
url = {https://www.naturvardsverket.se/publikationer/7000/978-91-620-7086-1/},
year = {2022}
}
@inproceedings{Martinsson2022,
author = {Martinsson, John and Willbo, Martin and Pirinen, Aleksis and Mogren, Olof and Sandsten, Maria},
booktitle = {The 7th Workshop on Detection and Classification of Acoustic Scenes and Events},
file = {:home/john/Downloads/DCASE2022Workshop_Martinsson_13.pdf:pdf},
number = {November},
pages = {2--6},
title = ,
url = {https://dcase.community/documents/workshop2022/proceedings/DCASE2022Workshop_Martinsson_13.pdf},
year = {2022}
}
@article{Martinsson2021,
abstract = {Data privacy is an increasingly important aspect of many real-world analytics tasks. Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output. Furthermore, finding the right balance between privacy and utility is often a tricky trade-off. In this work, we propose a novel approach for data privatization, which involves two steps: in the first step, it removes the sensitive information, and in the second step, it replaces this information with an independent random sample. Our method builds on adversarial representation learning which ensures strong privacy by training the model to fool an increasingly strong adversary. While previous methods only aim at obfuscating the sensitive information, we find that adding new random information in its place strengthens the provided privacy and provides better utility at any given level of privacy. The result is an approach that can provide stronger privatization on image data, and yet be preserving both the domain and the utility of the inputs, entirely independent of the downstream task.},
archiveprefix = {arXiv},
arxivid = {2006.08039},
author = {Martinsson, John and Zec, Edvin Listo and Gillblad, Daniel and Mogren, Olof},
doi = {10.1109/BigData52589.2021.9671802},
eprint = {2006.08039},
file = {:home/john/Downloads/2006.08039.pdf:pdf},
isbn = {9781665439022},
journal = {Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021},
keywords = {Deep Learning,Generative Adversarial Privacy,Machine Learning,Privacy},
pages = {1291--1299},
title = ,
url = {https://ieeexplore.ieee.org/document/9671802},
year = {2021}
}
@article{Martinsson2020,
author = {Martinsson, John and Schliep, Alexander and Eliasson, Bj{\"{o}}rn and Mogren, Olof},
doi = {10.1007/s41666-019-00059-y},
file = {:home/john/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Martinsson et al. - 2020 - Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks.pdf:pdf},
issn = {2509-4971},
journal = {Journal of Healthcare Informatics Research},
keywords = {blood glucose prediction,recurrent neural networks,type 1 diabetes},
number = {1},
pages = {1--18},
title = ,
url = {https://link.springer.com/article/10.1007/s41666-019-00059-y},
volume = {4},
year = {2020}
}
@article{Martinsson2020a,
abstract = {In this work, we approach the problem of semantically segmenting fashion images into different categories of clothing. This problem poses particular challenges because of the importance of both textural information and cues from shapes and context. To this end, we propose a fully convolutional neural network based on feature pyramid networks (FPN), together with a backbone consisting of the ResNeXt architecture. Our experimental evaluation shows that the proposed model achieves state-of-the-art results on two standard fashion benchmark datasets, and a qualitative study verifies its effectiveness when applied to typical fashion images. The approach has a modest memory footprint and can be used without a conditional random field (CRF) without much degradation of quality which makes our model preferable from a computational perspective. When comparing all methods without a CRF, our approach outperforms all state-of-the-art models on both datasets by a clear margin in all evaluated metrics. In fact, our approach achieves a higher accuracy without the CRF than the state-of-the-art models using CRFs.},
author = {Martinsson, John and Mogren, Olof},
doi = {10.1109/iccvw.2019.00382},
file = {:home/john/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Martinsson, Mogren - 2020 - Semantic Segmentation of Fashion Images Using Feature Pyramid Networks.pdf:pdf},
journal = {Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019},
pages = {3133--3136},
title = ,
url = {https://ieeexplore.ieee.org/document/9022176},
year = {2020}
}
@article{Korneliusson2020,
abstract = {In this work, we propose a method to generatively model the joint distribution of images and corresponding semantic segmentation maps using generative adversarial networks. We extend the Style-GAN architecture by iteratively growing the network during training, to add new output channels that model the semantic segmentation maps. We train the proposed method on a large dataset of fashion images and our experimental evaluation shows that the model produces samples that are coherent and plausible with semantic segmentation maps that closely match the semantics in the image.},
author = {Korneliusson, Marie and Martinsson, John and Mogren, Olof},
doi = {10.1109/iccvw.2019.00391},
file = {:home/john/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Korneliusson, Martinsson, Mogren - 2020 - Generative Modelling of Semantic Segmentation Data in the Fashion Domain.pdf:pdf},
isbn = {9781728150239},
journal = {Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019},
keywords = {Artificial neural networks,Clothing parsing,Deep learning,Henerative adversarial networks,Semantic segmentations},
pages = {3169--3172},
title = ,
url = {https://ieeexplore.ieee.org/document/9022286},
year = {2020}
}
@inproceedings{Martinsson2018a,
abstract = {Low-cost sensors continuously measuring blood glucose levels in intervals of a few minutes and mobile platforms combined with machine-learning (ML) solutions enable personalized precision health and disease management. ML solutions must be adapted to different sensor technologies, analysis tasks and individuals. This raises the issue of scale for creating such adapted ML solutions. We present an approach for predicting blood glucose levels for diabetics up to one hour into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. The model outputs the prediction along with an estimate of its certainty, helping users to interpret the predicted levels. The approach needs no feature engineering or data pre-processing, and is computationally inexpensive.},
author = {Martinsson, John and Schliep, Alexander and Eliasson, Bj{\"{o}}rn and Meijner, Christian and Persson, Simon and Mogren, Olof},
booktitle = {CEUR Workshop Proceedings},
file = {:home/john/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Martinsson et al. - 2018 - Automatic blood glucose prediction with confidence using recurrent neural networks.pdf:pdf},
issn = {16130073},
pages = {64--68},
title = ,
url = {https://ceur-ws.org/Vol-2148/paper10.pdf},
volume = {2148},
year = {2018}
}
@article{Martinsson2018,
abstract = {The safety of autonomous vehicles needs to be verified and validated by rigorous testing. It is expensive to test autonomous vehicles in the field, and therefore virtual testing methods are needed. Generative models of maneuvers such as cut-ins, overtakes, and lane-keeping are needed to thoroughly test the autonomous vehicle in a virtual environment. To train such models we need ground truth maneuver labels and obtaining such labels can be time-consuming and costly. In this work, we use a mixture of hidden Markov models to find clusters in maneuver trajectories, which can be used to speed up the labeling process. The maneuver trajectories are noisy, asynchronous and of uneven length, which make hidden Markov models a good fit for the data. The method is evaluated on labeled data from a test track consisting of cut-ins and overtakes with favorable results. Further, it is applied to natural data where many of the clusters found can be interpreted as driver maneuvers under reasonable assumptions. We show that mixtures of hidden Markov models can be used to find motion patterns in driver maneuver data from highways and country roads.},
author = {Martinsson, John and Mohammadiha, Nasser and Schliep, Alexander},
doi = {10.1109/ITSC.2018.8569418},
isbn = {9781728103235},
journal = {IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
pages = {3698--3705},
title = ,
url = {https://ieeexplore.ieee.org/document/8569418},
volume = {2018-Novem},
year = {2018}
}
@techreport{Martinsson2017,
abstract = {An area of interest in ecology is monitoring animal populations to better understand their behavior, biodiversity, and population dynamics. Acoustically active animals can be automatically classified by their sounds, and a particularly useful ecological indicator is the bird, as it responds quickly to changes in its environment. The aim of this study is to improve upon the state-of-the-art bird species classifier [1], which is implemented and used as a baseline. The questions asked are: Can deep residual neural networks learn to classify bird species based on bird song and how well to they perform? Do multiple-width frequency-delta data augmentation or meta-data fusion further increase the accuracy of the model? The questions are answered by training a deep residual neural network on one of the largest bird song data sets in the world, with and without the use of multiple- width frequency-delta data augmentation and meta-data fusion, and by comparing the results with the baseline. The study shows that deep residual neural networks can learn to classify bird species based on bird song and that the mean average precision of the classifier nearly matches the state-of-the-art. We further develop a proof of concept for meta-data fusion which indicates that fusion of elevation data can be used to increase the accuracy of the model, and in particular decrease its coverage error. Possible ways forward are to tune the hyper parameters of the deep residual neural network, fuse time of recording and geological location data into the model, or to move towards the more realistic, but less studied, open set problem of continuous classification rather than the N-class problem which is studied in this thesis. Keywords:},
author = {Martinsson, John},
file = {:home/john/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Martinsson - 2017 - Bird Species Identification using Convolutional Neural Networks.pdf:pdf},
institution = {Chalmers University of Technology},
pages = {68},
title = ,
url = {http://publications.lib.chalmers.se/records/fulltext/249467/249467.pdf},
year = {2017}
}
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