arXiv subjects

Xuenan Xu

Publications and source records attributed to Xuenan Xu.

At least 55 records · Page 3Linked to original sources

arXiv2024

DiveSound: LLM-Assisted Automatic Taxonomy Construction for Diverse Audio Generation

Audio generation has attracted significant attention. Despite remarkable enhancement in audio quality, existing models overlook diversity evaluation. This is partially due to the lack of a systematic sound class diversity framework and a matching dataset. To address these issues, we propose DiveSound, a novel framework for constructing multimodal datasets with in-class diversified taxonomy, assisted by large language models. As both textual and visual information can be utilized to guide diverse generation, DiveSound leverages multimodal contrastive representations in data construction. Our framework is highly autonomous and can be easily scaled up. We provide a textaudio-image aligned diversity dataset whose sound event class tags have an average of 2.42 subcategories. Text-to-audio experiments on the constructed dataset show a substantial increase of diversity with the help of the guidance of visual information.

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arXiv2024

Enhance Temporal Relations in Audio Captioning with Sound Event Detection

Automated audio captioning aims at generating natural language descriptions for given audio clips, not only detecting and classifying sounds, but also summarizing the relationships between audio events. Recent research advances in audio captioning have introduced additional guidance to improve the accuracy of audio events in generated sentences. However, temporal relations between audio events have received little attention while revealing complex relations is a key component in summarizing audio content. Therefore, this paper aims to better capture temporal relationships in caption generation with sound event detection (SED), a task that locates events' timestamps. We investigate the best approach to integrate temporal information in a captioning model and propose a temporal tag system to transform the timestamps into comprehensible relations. Results evaluated by the proposed temporal metrics suggest that great improvement is achieved in terms of temporal relation generation.

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arXiv2024

Towards Weakly Supervised Text-to-Audio Grounding

Text-to-audio grounding (TAG) task aims to predict the onsets and offsets of sound events described by natural language. This task can facilitate applications such as multimodal information retrieval. This paper focuses on weakly-supervised text-to-audio grounding (WSTAG), where frame-level annotations of sound events are unavailable, and only the caption of a whole audio clip can be utilized for training. WSTAG is superior to strongly-supervised approaches in its scalability to large audio-text datasets. Two WSTAG frameworks are studied in this paper: sentence-level and phrase-level. First, we analyze the limitations of mean pooling used in the previous WSTAG approach and investigate the effects of different pooling strategies. We then propose phrase-level WSTAG to use matching labels between audio clips and phrases for training. Advanced negative sampling strategies and self-supervision are proposed to enhance the accuracy of the weak labels and provide pseudo strong labels. Experimental results show that our system significantly outperforms the previous WSTAG SOTA. Finally, we conduct extensive experiments to analyze the effects of several factors on phrase-level WSTAG. The code and model is available at https://github.com/wsntxxn/TextToAudioGrounding.

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arXiv2024

PicoAudio: Enabling Precise Timestamp and Frequency Controllability of Audio Events in Text-to-audio Generation

Recently, audio generation tasks have attracted considerable research interests. Precise temporal controllability is essential to integrate audio generation with real applications. In this work, we propose a temporal controlled audio generation framework, PicoAudio. PicoAudio integrates temporal information to guide audio generation through tailored model design. It leverages data crawling, segmentation, filtering, and simulation of fine-grained temporally-aligned audio-text data. Both subjective and objective evaluations demonstrate that PicoAudio dramantically surpasses current state-of-the-art generation models in terms of timestamp and occurrence frequency controllability. The generated samples are available on the demo website https://zeyuxie29.github.io/PicoAudio.github.io.

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arXiv2024

AudioTime: A Temporally-aligned Audio-text Benchmark Dataset

Recent advancements in audio generation have enabled the creation of high-fidelity audio clips from free-form textual descriptions. However, temporal relationships, a critical feature for audio content, are currently underrepresented in mainstream models, resulting in an imprecise temporal controllability. Specifically, users cannot accurately control the timestamps of sound events using free-form text. We acknowledge that a significant factor is the absence of high-quality, temporally-aligned audio-text datasets, which are essential for training models with temporal control. The more temporally-aligned the annotations, the better the models can understand the precise relationship between audio outputs and temporal textual prompts. Therefore, we present a strongly aligned audio-text dataset, AudioTime. It provides text annotations rich in temporal information such as timestamps, duration, frequency, and ordering, covering almost all aspects of temporal control. Additionally, we offer a comprehensive test set and evaluation metric to assess the temporal control performance of various models. Examples are available on the https://zeyuxie29.github.io/AudioTime/

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arXiv2024

FakeSound: Deepfake General Audio Detection

With the advancement of audio generation, generative models can produce highly realistic audios. However, the proliferation of deepfake general audio can pose negative consequences. Therefore, we propose a new task, deepfake general audio detection, which aims to identify whether audio content is manipulated and to locate deepfake regions. Leveraging an automated manipulation pipeline, a dataset named FakeSound for deepfake general audio detection is proposed, and samples can be viewed on website https://FakeSoundData.github.io. The average binary accuracy of humans on all test sets is consistently below 0.6, which indicates the difficulty humans face in discerning deepfake audio and affirms the efficacy of the FakeSound dataset. A deepfake detection model utilizing a general audio pre-trained model is proposed as a benchmark system. Experimental results demonstrate that the performance of the proposed model surpasses the state-of-the-art in deepfake speech detection and human testers.

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arXiv2024

Zero-Shot Audio Captioning Using Soft and Hard Prompts

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space.In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP.We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method.

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arXiv2024

T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining

Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classification tasks. However, current CLAP struggles to capture temporal information within audio and text features, presenting substantial limitations for tasks such as audio retrieval and generation. To address this gap, we introduce T-CLAP, a temporal-enhanced CLAP model. We use Large Language Models~(LLMs) and mixed-up strategies to generate temporal-contrastive captions for audio clips from extensive audio-text datasets. Subsequently, a new temporal-focused contrastive loss is designed to fine-tune the CLAP model by incorporating these synthetic data. We conduct comprehensive experiments and analysis in multiple downstream tasks. T-CLAP shows improved capability in capturing the temporal relationship of sound events and outperforms state-of-the-art models by a significant margin.

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arXiv2024

A Detailed Audio-Text Data Simulation Pipeline using Single-Event Sounds

Recently, there has been an increasing focus on audio-text cross-modal learning. However, most of the existing audio-text datasets contain only simple descriptions of sound events. Compared with classification labels, the advantages of such descriptions are significantly limited. In this paper, we first analyze the detailed information that human descriptions of audio may contain beyond sound event labels. Based on the analysis, we propose an automatic pipeline for curating audio-text pairs with rich details. Leveraging the property that sounds can be mixed and concatenated in the time domain, we control details in four aspects: temporal relationship, loudness, speaker identity, and occurrence number, in simulating audio mixtures. Corresponding details are transformed into captions by large language models. Audio-text pairs with rich details in text descriptions are thereby obtained. We validate the effectiveness of our pipeline with a small amount of simulated data, demonstrating that the simulated data enables models to learn detailed audio captioning.

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arXiv2024

BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data

Compared with ample visual-text pre-training research, few works explore audio-text pre-training, mostly due to the lack of sufficient parallel audio-text data. Most existing methods incorporate the visual modality as a pivot for audio-text pre-training, which inevitably induces data noise. In this paper, we propose to utilize audio captioning to generate text directly from audio, without the aid of the visual modality so that potential noise from modality mismatch is eliminated. Furthermore, we propose caption generation under the guidance of AudioSet tags, leading to more accurate captions. With the above two improvements, we curate high-quality, large-scale parallel audio-text data, based on which we perform audio-text pre-training. We comprehensively demonstrate the performance of the pre-trained model on a series of downstream audio-related tasks, including single-modality tasks like audio classification and tagging, as well as cross-modal tasks consisting of audio-text retrieval and audio-based text generation. Experimental results indicate that our approach achieves state-of-the-art zero-shot classification performance on most datasets, suggesting the effectiveness of our synthetic data. The audio encoder also serves as an efficient pattern recognition model by fine-tuning it on audio-related tasks. Synthetic data and pre-trained models are available online.

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arXiv2024

Enhancing Audio Generation Diversity with Visual Information

Audio and sound generation has garnered significant attention in recent years, with a primary focus on improving the quality of generated audios. However, there has been limited research on enhancing the diversity of generated audio, particularly when it comes to audio generation within specific categories. Current models tend to produce homogeneous audio samples within a category. This work aims to address this limitation by improving the diversity of generated audio with visual information. We propose a clustering-based method, leveraging visual information to guide the model in generating distinct audio content within each category. Results on seven categories indicate that extra visual input can largely enhance audio generation diversity. Audio samples are available at https://zeyuxie29.github.io/DiverseAudioGeneration.

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arXiv2023

Beyond the Status Quo: A Contemporary Survey of Advances and Challenges in Audio Captioning

Automated audio captioning (AAC), a task that mimics human perception as well as innovatively links audio processing and natural language processing, has overseen much progress over the last few years. AAC requires recognizing contents such as the environment, sound events and the temporal relationships between sound events and describing these elements with a fluent sentence. Currently, an encoder-decoder-based deep learning framework is the standard approach to tackle this problem. Plenty of works have proposed novel network architectures and training schemes, including extra guidance, reinforcement learning, audio-text self-supervised learning and diverse or controllable captioning. Effective data augmentation techniques, especially based on large language models are explored. Benchmark datasets and AAC-oriented evaluation metrics also accelerate the improvement of this field. This paper situates itself as a comprehensive survey covering the comparison between AAC and its related tasks, the existing deep learning techniques, datasets, and the evaluation metrics in AAC, with insights provided to guide potential future research directions.

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arXiv2023

Improving Audio Caption Fluency with Automatic Error Correction

Automated audio captioning (AAC) is an important cross-modality translation task, aiming at generating descriptions for audio clips. However, captions generated by previous AAC models have faced ``false-repetition'' errors due to the training objective. In such scenarios, we propose a new task of AAC error correction and hope to reduce such errors by post-processing AAC outputs. To tackle this problem, we use observation-based rules to corrupt captions without errors, for pseudo grammatically-erroneous sentence generation. One pair of corrupted and clean sentences can thus be used for training. We train a neural network-based model on the synthetic error dataset and apply the model to correct real errors in AAC outputs. Results on two benchmark datasets indicate that our approach significantly improves fluency while maintaining semantic information.

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arXiv2023

Diverse and Vivid Sound Generation from Text Descriptions

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.

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arXiv2022

Audio-text Retrieval in Context

Audio-text retrieval based on natural language descriptions is a challenging task. It involves learning cross-modality alignments between long sequences under inadequate data conditions. In this work, we investigate several audio features as well as sequence aggregation methods for better audio-text alignment. Moreover, through a qualitative analysis we observe that semantic mapping is more important than temporal relations in contextual retrieval. Using pre-trained audio features and a descriptor-based aggregation method, we build our contextual audio-text retrieval system. Specifically, we utilize PANNs features pre-trained on a large sound event dataset and NetRVLAD pooling, which directly works with averaged descriptors. Experiments are conducted on the AudioCaps and CLOTHO datasets, and results are compared with the previous state-of-the-art system. With our proposed system, a significant improvement has been achieved on bidirectional audio-text retrieval, on all metrics including recall, median and mean rank.

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arXiv2022

Can Audio Captions Be Evaluated with Image Caption Metrics?

Automated audio captioning aims at generating textual descriptions for an audio clip. To evaluate the quality of generated audio captions, previous works directly adopt image captioning metrics like SPICE and CIDEr, without justifying their suitability in this new domain, which may mislead the development of advanced models. This problem is still unstudied due to the lack of human judgment datasets on caption quality. Therefore, we firstly construct two evaluation benchmarks, AudioCaps-Eval and Clotho-Eval. They are established with pairwise comparison instead of absolute rating to achieve better inter-annotator agreement. Current metrics are found in poor correlation with human annotations on these datasets. To overcome their limitations, we propose a metric named FENSE, where we combine the strength of Sentence-BERT in capturing similarity, and a novel Error Detector to penalize erroneous sentences for robustness. On the newly established benchmarks, FENSE outperforms current metrics by 14-25% accuracy. Code, data and web demo available at: https://github.com/blmoistawinde/fense

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arXiv2021

Voice activity detection in the wild: A data-driven approach using teacher-student training

Voice activity detection is an essential pre-processing component for speech-related tasks such as automatic speech recognition (ASR). Traditional supervised VAD systems obtain frame-level labels from an ASR pipeline by using, e.g., a Hidden Markov model. These ASR models are commonly trained on clean and fully transcribed data, limiting VAD systems to be trained on clean or synthetically noised datasets. Therefore, a major challenge for supervised VAD systems is their generalization towards noisy, real-world data. This work proposes a data-driven teacher-student approach for VAD, which utilizes vast and unconstrained audio data for training. Unlike previous approaches, only weak labels during teacher training are required, enabling the utilization of any real-world, potentially noisy dataset. Our approach firstly trains a teacher model on a source dataset (Audioset) using clip-level supervision. After training, the teacher provides frame-level guidance to a student model on an unlabeled, target dataset. A multitude of student models trained on mid- to large-sized datasets are investigated (Audioset, Voxceleb, NIST SRE). Our approach is then respectively evaluated on clean, artificially noised, and real-world data. We observe significant performance gains in artificially noised and real-world scenarios. Lastly, we compare our approach against other unsupervised and supervised VAD methods, demonstrating our method's superiority.

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arXiv2021

Investigating Local and Global Information for Automated Audio Captioning with Transfer Learning

Automated audio captioning (AAC) aims at generating summarizing descriptions for audio clips. Multitudinous concepts are described in an audio caption, ranging from local information such as sound events to global information like acoustic scenery. Currently, the mainstream paradigm for AAC is the end-to-end encoder-decoder architecture, expecting the encoder to learn all levels of concepts embedded in the audio automatically. This paper first proposes a topic model for audio descriptions, comprehensively analyzing the hierarchical audio topics that are commonly covered. We then explore a transfer learning scheme to access local and global information. Two source tasks are identified to respectively represent local and global information, being Audio Tagging (AT) and Acoustic Scene Classification (ASC). Experiments are conducted on the AAC benchmark dataset Clotho and Audiocaps, amounting to a vast increase in all eight metrics with topic transfer learning. Further, it is discovered that local information and abstract representation learning are more crucial to AAC than global information and temporal relationship learning.

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