Michiel Jacobs (michiel-j)

michiel-j

Geek Repo

Company:KU Leuven

Location:Belgium

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Michiel Jacobs's repositories

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AST

Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

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BNAIC-2024_Comparison_of_ICBHI_models

Code accompanying BNAIC-2024 submission

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DCASE23-lightSED

Implementation of paper "Frequency and Channel Attention for Computationally Efficient Sound Event Detection".

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DCASE23_gomin

GOMIN; Gaudio Open Mel-spectrogram Inversion Network

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DCASE23_Task5_SCL

System that ranked 2nd in DCASE 2023 Challenge Task 5: Few-shot Bioacoustic Event Detection

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griffin_lim

Implementation of the Griffin and Lim algorithm to recover an audio signal from a magnitude-only spectrogram.

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Hands-On-Computer-Vision-with-TensorFlow-2

Hands-On Computer Vision with TensorFlow 2, published by Packt

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HierarchicalSpectrogramTransformer

Official implementation of Hierarchical Spectrogram Transformers (HST)

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KielUniversity_DL-Approach-Gait-Event-Detection

Gait events detection from wearable inertial measurement units based on a temporal convolutional neural network

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melgan-neurips

GAN-based Mel-Spectrogram Inversion Network for Text-to-Speech Synthesis

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pyshoe

An open source foot-mounted INS accompanied by a comprehensive inertial dataset.

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SCL_ICBHI2017

This is the official PyTorch implementation of our work Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning (WASPAA 2023)

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SmartGait

Gait Phase Estimation using Artificial Intelligence

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SNNs

Tutorials and implementations for "Self-normalizing networks". Paper by G. Klambauer, T. Unterthiner, A. Mayr and S. Hochreiter.

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SPSI_Python

Single Pass Spectrogram Inversion in a Jupyter Python notebook

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TRIPOD

Gait analysis data pipeline for publication "TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis".

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Wheeze_Counter

Code of paper "Real-Time Counting of Wheezing Events From Lung Sounds Using Deep Learning Algorithms: Implications for disease prediction and early intervention", by Sunghoon Im, Taewi Kim, et al.

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