Generating Music Medleys via Playing Music Puzzle Games

Generating music medleys is about finding an optimal permutation of a given set of music clips. Toward this goal, we propose a self-supervised learning task, called the music puzzle game, to train neural network models to learn the sequential patterns in music. In essence, such a game requires machines to correctly sort a few multisecond music fragments. In the training stage, we learn the model by sampling multiple non-overlapping fragment pairs from the same songs and seeking to predict whether a given pair is consecutive and is in the correct chronological order. For testing, we design a number of puzzle games with different difficulty levels, the most difficult one being music medley, which requiring sorting fragments from different songs. On the basis of state-of-the-art Siamese convolutional network, we propose an improved architecture that learns to embed frame-level similarity scores computed from the input fragment pairs to a common space, where fragment pairs in the correct order can be more easily identified. Our result shows that the resulting model, dubbed as the similarity embedding network (SEN), performs better than competing models across different games, including music jigsaw puzzle, music sequencing, and music medley. Example results can be found at our project website, https://remyhuang.github.io/DJnet.Comment: Accepted at AAAI 201

Pop Music Highlighter: Marking the Emotion Keypoints

The goal of music highlight extraction is to get a short consecutive segment of a piece of music that provides an effective representation of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs. The rationale behind that approach is that the highlight of a song is usually the most emotional part. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods against a heuristic energy-based method, a structural repetition-based method, and a few other simple feature-based methods for this task. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC POP 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate the effectiveness of our methods over competing methods. For reproducibility, we open source the code and pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.Comment: Transactions of the ISMIR vol. 1, no.

Delving into Motion-Aware Matching for Monocular 3D Object Tracking

Recent advances of monocular 3D object detection facilitate the 3D multi-object tracking task based on low-cost camera sensors. In this paper, we find that the motion cue of objects along different time frames is critical in 3D multi-object tracking, which is less explored in existing monocular-based approaches. In this paper, we propose a motion-aware framework for monocular 3D MOT. To this end, we propose MoMA-M3T, a framework that mainly consists of three motion-aware components. First, we represent the possible movement of an object related to all object tracklets in the feature space as its motion features. Then, we further model the historical object tracklet along the time frame in a spatial-temporal perspective via a motion transformer. Finally, we propose a motion-aware matching module to associate historical object tracklets and current observations as final tracking results. We conduct extensive experiments on the nuScenes and KITTI datasets to demonstrate that our MoMA-M3T achieves competitive performance against state-of-the-art methods. Moreover, the proposed tracker is flexible and can be easily plugged into existing image-based 3D object detectors without re-training. Code and models are available at https://github.com/kuanchihhuang/MoMA-M3T.Comment: Accepted by ICCV 2023. Code is available at https://github.com/kuanchihhuang/MoMA-M3

MUTING THE LOUDEST VOICES: OVERREPRESENTATION, EXCLUSION, AND DEMOCRACY

Past research on political representation tends to treat overrepresentation simply as another way to describe underrepresentation. In this article, I challenge the lack of sustained theoretical attention to the overrepresentation of privileged groups. Overrepresentation differs from underrepresentation in two aspects: (i) it gives rise to a different normative standpoint, for it questions the excessive presence of dominant groups; (ii) members of overrepresented groups are homogenous in a distinctive way, which means that a peculiar set of advantages gives a very small group greater political power. To restore the democratic commitment to equal citizenship, I argue that we should endorse the politics of exclusion to limit the political influence of overrepresented groups. To ensure that exclusion devices are in line with this normative goal, I maintain that exclusion measures need to operate on an intersectional basis, and that to justify the targeted cleavages, a “plausible structural story” needs to be told.Master of Scienc

Adhesion-induced lateral phase separation of multi-component membranes: the effect of repellers and confinement

We present a theoretical study for adhesion-induced lateral phase separation for a membrane with short stickers, long stickers and repellers confined between two hard walls. The effects of confinement and repellers on lateral phase separation are investigated. We find that the critical potential depth of the stickers for lateral phase separation increases as the distance between the hard walls decreases. This suggests confinement-induced or force-induced mixing of stickers. We also find that stiff repellers tend to enhance, while soft repellers tend to suppress adhesion-induced lateral phase separation

Does Labor Market Rigidity Matter for Economic Performance? Evidence from the Four Asian Tigers

With the increments of labor market institutions, the potential problem caused by labor market rigidity is emerging within the four Asian tigers, namely, Hong Kong, South Korea, Singapore and Taiwan. This study emphasizes the impact of labor market rigidity on economic performance in the four Asian tigers over the 1980-2010 period. Through the estimation of the aggregate production function, we find that labor market rigidity has a negative impact on output and economic growth. On the other hand, without imposing any labor market institutional adjustment that would lower the standard of labor conditions, the rises in country’s competitiveness can serve as a balancing force to mitigate the negative impacts of labor market rigidity. A crucial insight for policymakers is to determine the most efficient method for giving labor effective protection without hurting economic performance

Labor Market Reforms on the Unemployment Rate and Wage Payments in Europe

In contrast to the existing literature that focuses only on high unemployment driven by labor market regulations, this study emphasizes the impact of labor market regulations on wage payments in 15 European countries over the period 1985 to 2009. Through a simultaneous system of labor demand and supply, we find that the effect of labor market institutions on the behavior of labor demand outweighs the effect on labor suppliers, which pushes up the wage rate and mitigates the unemployment problem. By detailed investigations into all the responses of players in the labor market, it is plausible for policymakers in Europe to figure out the most efficient method for lowering the unemployment rate without hurting wage payments or discouraging labor supply