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Design Choices for Learning Embeddings from Auxiliary Tasks for Domain Generalization in Anomalous Sound Detection

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023, p.1-5

Wilkinghoff, Kevin

2023

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Self-Supervised Learning for Anomalous Sound Detection

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, p.276-280

Wilkinghoff, Kevin

2024

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Why Do Angular Margin Losses Work Well for Semi-Supervised Anomalous Sound Detection?

IEEE/ACM transactions on audio, speech, and language processing, 2024-01, Vol.32, p.1-15

2024

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F1-EV score: Measuring The Likelihood of Estimating a Good Decision Threshold for Semi-Supervised Anomaly Detection

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, p.256-260

2024

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TACos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024, p.9941-9945

2024

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Robust Detection of Jittered Multiply Repeating Audio Events Using Iterated Time-Warped ACF

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, p.276-280

2018

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Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection

2021 International Joint Conference on Neural Networks (IJCNN), 2021, p.1-8

Wilkinghoff, Kevin

2021

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Speech Recognition Lab

Procedia computer science, 2022, Vol.205, p.218-228

2022

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On Open-Set Classification with L3-Net Embeddings for Machine Listening Applications

2020 28th European Signal Processing Conference (EUSIPCO), 2021, p.800-804

Wilkinghoff, Kevin

2021

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On Using Pre-Trained Embeddings for Detecting Anomalous Sounds with Limited Training Data

2023 31st European Signal Processing Conference (EUSIPCO), 2023, p.186-190

2023

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Multi-Sample Dynamic Time Warping for Few-Shot Keyword Spotting

2024 32nd European Signal Processing Conference (EUSIPCO), 2024, p.166-170

2024

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Strong Label Generation for Preparing Speech Data in Military Applications Using CTC Loss

2024 International Conference on Military Communication and Information Systems (ICMCIS), 2024, p.1-7

2024

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Novel Generative Classifier for Acoustic Events

2023 31st European Signal Processing Conference (EUSIPCO), 2023, p.196-200

2023

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Analyzing the Impact of HF-Specific Signal Degradation on Automatic Speech Recognition

2024 International Conference on Military Communication and Information Systems (ICMCIS), 2024, p.1-10

2024

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Towards Human-Machine Integration for Signals Intelligence Applications

2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2022, p.1-6

2022

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Glottal mixture model (GLOMM) for speaker identification on telephone channels

2017 25th European Signal Processing Conference (EUSIPCO), 2017, p.2734-2738

2017

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Design Choices for Learning Embeddings from Auxiliary Tasks for Domain Generalization in Anomalous Sound Detection

Self-Supervised Learning for Anomalous Sound Detection

Why Do Angular Margin Losses Work Well for Semi-Supervised Anomalous Sound Detection?

F1-EV score: Measuring The Likelihood of Estimating a Good Decision Threshold for Semi-Supervised Anomaly Detection

TACos: Learning Temporally Structured Embeddings for Few-Shot Keyword Spotting with Dynamic Time Warping

Robust Detection of Jittered Multiply Repeating Audio Events Using Iterated Time-Warped ACF

Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection

Speech Recognition Lab

On Open-Set Classification with L3-Net Embeddings for Machine Listening Applications

On Using Pre-Trained Embeddings for Detecting Anomalous Sounds with Limited Training Data

Multi-Sample Dynamic Time Warping for Few-Shot Keyword Spotting

Strong Label Generation for Preparing Speech Data in Military Applications Using CTC Loss

Novel Generative Classifier for Acoustic Events

Analyzing the Impact of HF-Specific Signal Degradation on Automatic Speech Recognition

Towards Human-Machine Integration for Signals Intelligence Applications

Glottal mixture model (GLOMM) for speaker identification on telephone channels

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