Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific audio generation by using natural language description as a clue to generate broad sounds. Unlike visual information, a text description is concise by its nature but has rich hidden meanings beneath, which poses a higher possibility and complexity on the audio to be generated. A Variation-Quantized GAN is used to train a codebook learning discrete representations of spectrograms. For a given text description, its pre-trained embedding is fed to a Transformer to sample codebook indices to decode a spectrogram to be further transformed into waveform by a melgan vocoder. The generated waveform has high quality and fidelity while excellently corresponding to the given text. Experiments show that our proposed method is capable of generating natural, vivid audios, achieving superb quantitative and qualitative results.

Previous audio enhancement training usually requires clean signals with additive noises; hence commonly focuses on speech enhancement, where clean speech is easy to access. This paper goes beyond a broader sound event enhancement by using a weakly supervised approach via sound event detection (SED) to approximate the location and presence of a specific sound event. We propose a category-adapted system to enable enhancement on any selected sound category, where we first familiarize the model to all common sound classes and followed by a category-specific fine-tune procedure to enhance the targeted sound class. Evaluation is conducted on ten common sound classes, with a comparison to traditional and weakly supervised enhancement methods. Results indicate an average 2.86 dB SDR increase, with more significant improvement on speech (9.15 dB), music (5.01 dB), and typewriter (3.68 dB) under SNR of 0 dB. All enhancement metrics outperform previous weakly supervised methods and achieve comparable results to the state-of-the-art method that requires clean signals.

Previous audio-visual (AV) alignment mainly focuses on frame-level synchronization while neglecting clip-wise matching. We focus on AV parsing on fully unconstrained data where the audio and visual events do not necessarily co-present. A video-enhanced Audioset dataset is provided to investigate parsing on such a mismatching setting, with 376 events included. To our knowledge, this is the first time where AV event parsing and detection are inspected on a clip-wise matching scenario. Experiments show that our proposed method largely improves video parsing accuracy on tagging and detection. Further, a parsing model pre-trained on our dataset can assist in accurately locating audio-visual syncing time spans.

Static analysis tools, although widely adopted in industry, suffer from a high false positive rate. This paper aims to refine the results of static analysis tools, by automatically searching for a vulnerable path from given defect report. To realize this goal, we develop SATRACER, a novel tool which integrates symbolic execution techniques with static analysis. SATRACER selectively skips those program parts which can be consistently updated by static analysis, thus drastically improving performance. We have applied SATRACER to a set of 21 real-world applications. Evaluation results show that SATRACER can successfully remove 71.4% false alarms reported by a commercial static analysis tool in 10 hours, and confirmed 29 real use-after-free bugs and 895 real null-pointer-dereference bugs.

Go, a fast growing programming language, is often considered as “the programming language of the cloud”. The language provides a rich set of synchronization primitives, making it easy to write concurrent programs with great parallelism. However. the rich set of primitives also introduces many bugs. We build Gobench, the first benchmark suite for Go concurrency bugs. Currently, Gobench consists of 82 real bugs from 9 popular open source applications and 103 bug kernels. The bug kernels are carefully extracted and simplified from 67 out of these 82 bugs and 36 additional bugs reported in a recent study to preserve their bug-inducing complexities as much as possible. These bugs cover a variety of concurrency issues, both traditional and Go-specific. We believe Gobench will be instrumental in helping researchers understand concurrency bugs in Go and develop effective tools for their detection. We have therefore evaluated a range of representative concurrency error detection tools using Gobench. Our evaluation has revealed their limitations and provided insights for making further improvements.

Audio tagging aims at predicting sound events occurred in a recording. Traditional models require enormous laborious annotations, otherwise performance degeneration will be the norm. Therefore, we investigate robust audio tagging models in low-resource scenarios with the enhancement of knowledge graphs. Besides existing ontological knowledge, we further propose a semi-automatic approach that can construct temporal knowledge graphs on diverse domain-specific label sets. Moreover, we leverage a variant of relation-aware graph neural network, D-GCN, to combine the strength of the two knowledge types. Experiments on AudioSet and SONYC urban sound tagging datasets suggest the effectiveness of the introduced temporal knowledge, and the advantage of the combined KGs with D-GCN over single knowledge source.

Deep learning has been widely adopted in industry and has achieved great success in a wide range of application areas. Bugs in deep learning programs can cause catastrophic failures, in addition to a serious waste of resources and time.This paper aims at detecting industrial TensorFlow program bugs. We report an extensive empirical study on 12,289 failed TensorFlow jobs, showing that existing static tools can effectively detect 72.55% of the top three types of Python bugs in industrial TensorFlow programs. In addition, we propose (for the first time) a constraint-based approach for detecting TensorFlow shape-related errors (one of the most common TensorFlow-specific bugs), together with an associated tool, ShapeTracer. Our evaluation on a set of 60 industrial TensorFlow programs shows that ShapeTracer is efficient and effective: it analyzes each program in at most 3 seconds and detects effectively 40 out of 60 industrial TensorFlow program bugs, with no false positives. ShapeTracer has been deployed in the platform-X platform and will be released soon.

The frequent release of Android OS and its various versions bring many compatibility issues to Android Apps. This paper studies and addresses such evolution-induced compatibility problems. We conduct an extensive empirical study over 11 different Android versions and 4,936 Android Apps. Our study shows that there are drastic API changes between adjacent Android versions, with averagely 140.8 new types, 1,505.6 new methods, and 979.2 new fields being introduced in each release. However, the Android Support Library (provided by the Android OS) only supports less than 23% of the newly added methods, with much less support for new types and fields. As a result, 91.84% of Android Apps write additional code to support different OS versions. Furthermore, 88.65% of the supporting codes share a common pattern, which directly compares variable android.os.Build.VERSION.SDK_INT with a constant version number, to use an API of particular versions.