Qing Wang

Tongxi Wang, Yang Yu, Qing Wang et al.

Song generation is regarded as the most challenging problem in music AIGC; nonetheless, existing approaches have yet to fully overcome four persistent limitations: controllability, generalizability, perceptual quality, and duration. We argue that these shortcomings stem primarily from the prevailing paradigm of attempting to learn music theory directly from raw audio, a task that remains prohibitively difficult for current models. To address this, we present Bar-level AI Composing Helper (BACH), the first model explicitly designed for song generation through human-editable symbolic scores. BACH introduces a tokenization strategy and a symbolic generative procedure tailored to hierarchical song structure. Consequently, it achieves substantial gains in the efficiency, duration, and perceptual quality of song generation. Experiments demonstrate that BACH, with a small model size, establishes a new SOTA among all publicly reported song generation systems, even surpassing commercial solutions such as Suno. Human evaluations further confirm its superiority across multiple subjective metrics.

Junjie Wang, Mingyang Li, Song Wang et al.

Crowdtesting is effective especially when it comes to the feedback on GUI systems, or subjective opinions about features. Despite of this, we find crowdtesting reports are highly replicated, i.e., 82% of them are replicates of others. Hence automatically detecting replicate reports could help reduce triaging efforts. Most of the existing approaches mainly adopted textual information for replicate detection, and suffered from low accuracy because of the expression gap. Our observation on real industrial crowdtesting data found that when dealing with crowdtesting reports of GUI systems, the reports would accompanied with images, i.e., the screenshots of the app. We assume the screenshot to be valuable for replicate crowdtesting report detection because it reflects the real scenario of the failure and is not affected by the variety of natural languages. In this work, we propose a replicate detection approach, TSDetector, which combines information from the screenshots and the textual descriptions to detect replicate crowdtesting reports. We extract four types of features to characterize the screenshots and the textual descriptions, and design an algorithm to detect replicates based on four similarity scores derived from the four different features respectively. We investigate the effectiveness and advantage of TSDetector on 15 commercial projects with 4,172 reports from one of the Chinese largest crowdtesting platforms.Results show that TSDetector can outperform existing state-of-the-art approaches significantly. In addition, we also evaluate its usefulness using real-world case studies. The feedback from real-world testers demonstrates its practical value