Search Results for author: Manu Airaksinen

Found 7 papers, 4 papers with code

Modeling 3D Infant Kinetics Using Adaptive Graph Convolutional Networks

1 code implementation22 Feb 2024 Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Kivi, Leena Haataja, Sampsa Vanhatalo, Teemu Roos

Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions.

Pose Estimation

Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors

1 code implementation16 May 2023 Einari Vaaras, Manu Airaksinen, Sampsa Vanhatalo, Okko Räsänen

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.

Human Activity Recognition Self-Supervised Learning

Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition

1 code implementation21 Jun 2022 Einari Vaaras, Manu Airaksinen, Okko Räsänen

In this paper, we combine CPC and multiple dimensionality reduction methods in search of functioning practices for clustering-based AL. Our experiments for simulating speech emotion recognition system deployment show that both the local and global topology of the feature space can be successfully used for AL, and that CPC can be used to improve clustering-based AL performance over traditional signal features.

Active Learning Clustering +3

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensors

no code implementations21 Sep 2019 Manu Airaksinen, Okko Räsänen, Elina Ilén, Taru Häyrinen, Anna Kivi, Viviana Marchi, Anastasia Gallen, Sonja Blom, Anni Varhe, Nico Kaartinen, Leena Haataja, Sampsa Vanhatalo

These data were manually annotated for infant posture and movement based on video recordings of the sessions, and using a novel annotation scheme specifically designed to assess the overall movement pattern of infants in the given age group.

Speaker-independent raw waveform model for glottal excitation

no code implementations25 Apr 2018 Lauri Juvela, Vassilis Tsiaras, Bajibabu Bollepalli, Manu Airaksinen, Junichi Yamagishi, Paavo Alku

Recent speech technology research has seen a growing interest in using WaveNets as statistical vocoders, i. e., generating speech waveforms from acoustic features.

Speech Synthesis Text-To-Speech Synthesis +1

Speech waveform synthesis from MFCC sequences with generative adversarial networks

1 code implementation3 Apr 2018 Lauri Juvela, Bajibabu Bollepalli, Xin Wang, Hirokazu Kameoka, Manu Airaksinen, Junichi Yamagishi, Paavo Alku

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 generally considered unusable for speech synthesis.

Generative Adversarial Network Speech Synthesis

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