22 research outputs found
motilitAI: a machine learning framework for automatic prediction of human sperm motility
In this article, human semen samples from the Visem dataset are automatically assessed with machine learning methods for their quality with respect to sperm motility. Several regression models are trained to automatically predict the percentage (0–100) of progressive, non-progressive, and immotile spermatozoa. The videos are adopted for unsupervised tracking and two different feature extraction methods—in particular custom movement statistics and displacement features. We train multiple neural networks and support vector regression models on the extracted features. Best results are achieved using a linear Support Vector Regressor with an aggregated and quantized representation of individual displacement features of each sperm cell. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the mean absolute error (MAE) could be reduced from 8.83 to 7.31. We provide the source code for our experiments on GitHub (Code available at: https://github.com/EIHW/motilitAI)
EmoNet: a transfer learning framework for multi-corpus speech emotion recognition
In this manuscript, the topic of multi-corpus Speech Emotion Recognition
(SER) is approached from a deep transfer learning perspective. A large corpus
of emotional speech data, EmoSet, is assembled from a number of existing SER
corpora. In total, EmoSet contains 84181 audio recordings from 26 SER corpora
with a total duration of over 65 hours. The corpus is then utilised to create a
novel framework for multi-corpus speech emotion recognition, namely EmoNet. A
combination of a deep ResNet architecture and residual adapters is transferred
from the field of multi-domain visual recognition to multi-corpus SER on
EmoSet. Compared against two suitable baselines and more traditional training
and transfer settings for the ResNet, the residual adapter approach enables
parameter efficient training of a multi-domain SER model on all 26 corpora. A
shared model with only times the number of parameters of a model trained
on a single database leads to increased performance for 21 of the 26 corpora in
EmoSet. Measured by McNemar's test, these improvements are further significant
for ten datasets at while there are just two corpora that see only
significant decreases across the residual adapter transfer experiments.
Finally, we make our EmoNet framework publicly available for users and
developers at https://github.com/EIHW/EmoNet. EmoNet provides an extensive
command line interface which is comprehensively documented and can be used in a
variety of multi-corpus transfer learning settings.Comment: 18 pages, 7 figure