11 citations · 21 across the 7 of their papers we have counts for
8 papers
PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
Einari Vaaras, Manu Airaksinen, Okko Räsänen
Self-supervised learning (SSL) is a data-driven learning approach that utilizes the innate structure of the data to guide the learning process. In contrast to supervised learning,…
Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks
Daniel Holmberg, Manu Airaksinen, Viviana Marchi +5
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems that may need prompt interventions. Spontaneous motor activity, or '…
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors
Einari Vaaras, Manu Airaksinen, Sampsa Vanhatalo +1
The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings.…
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition
Einari Vaaras, Manu Airaksinen, Okko Räsänen
When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and expenses. For case…
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors
Manu Airaksinen, Sampsa Vanhatalo, Okko Räsänen
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis play…
Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors
Manu Airaksinen, Okko Räsänen, Elina Ilén +9
Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous sys…
Speaker-independent raw waveform model for glottal excitation
Lauri Juvela, Vassilis Tsiaras, Bajibabu Bollepalli +3
Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i.e., generating speech waveforms from acoustic features. These models have…
Speech waveform synthesis from MFCC sequences with generative adversarial networks
Lauri Juvela, Bajibabu Bollepalli, Xin Wang +4
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are ge…