Daolang is a doctoral student co-supervised by Samuel Kaski (Aalto University) and Luigi Acerbi (University of Helsinki) since July 2022. He is fully funded by Finnish Center for Artificial Intelligence (FCAI). Previously, Daolang worked as a research assistant in the Probabilistic Machine Learning Group at Aalto University. He received his master’s degree at Aalto University, with a major in Machine Learning, Data Science and Artificial Intelligence (Macadamia). In his spare time, he is also an electronic music producer, mainly producing house music. Daolang’s work focuses on amortized inference with applications to decision-making and multi-agent modeling.
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MSc in Machine Learning, 2022
BSc in Computer Science & Technology, 2020
Sound event localization and detection (SELD) is a multi-task learning problem that aims to detect different audio events and estimate their corresponding locations. All of the previously proposed SELD systems were based on human-extracted features such as Mel-spectrograms to make the prediction, which required speciﬁc prior knowledge in acoustics. In this report, we investigate the possibility to apply representation learning directly to the raw audio and propose an end-to-end sample-level SELD framework. To improve generalization, we applied three data augmentation tricks: sound ﬁeld rotation, time masking and random audio equalization. The proposed system is evaluated on the TAU-NIGENS Spatial Sound Events 2021 development dataset. The experimental results will be submitted to DCASE 2021 challenge task 3.
In recent years, Deep Neural Networks (DNN) have empowered Compressed Sensing (CS) substantially and have achieved high reconstruction quality and speed far exceeding traditional CS methods. However, there are still lots of issues to be further explored before it can be practical enough. There are mainly two challenging problems in CS, one is to achieve efficient data sampling, and the other is to reconstruct images with high-quality. To address the two challenges, this paper proposes a novel Runge-Kutta Convolutional Compressed Sensing Network (RK-CCSNet). In the sensing stage, RK-CCSNet applies Sequential Convolutional Module (SCM) to gradually compact measurements through a series of convolution filters. In the reconstruction stage, RK-CCSNet establishes a novel Learned Runge-Kutta Block (LRKB) based on the famous Runge-Kutta methods, reformulating the process of image reconstruction as a discrete dynamical system. Finally, the implementation of RK-CCSNet achieves state-of-the-art performance on influential benchmarks with respect to prestigious baselines, and all the codes are available at https://github.com/rkteddy/RK-CCSNet