Generating sound effects that people want is an important topic. However, there are limited studies in this area for sound generation. In this study, we investigate generating sound conditioned on a text prompt and propose a novel text-to-sound generation framework that consists of a text encoder, a Vector Quantized Variational Autoencoder (VQ-VAE), a token-decoder, and a vocoder. The framework first uses the token-decoder to transfer the text features extracted from the text encoder to a mel-spectrogram with the help of VQ-VAE, and then the vocoder is used to transform the generated mel-spectrogram into a waveform. We found that the token-decoder significantly influences the generation performance. Thus, we focus on designing a good token-decoder in this study. We begin with th21e traditional autoregressive (AR) token-decoder, which has shown state-of-the-art performance in previous sound generation works. However, the AR token-decoder always predicts the mel-spectrogram tokens one by one in order, which may introduce the unidirectional bias and accumulation of errors problems. Moreover, with the AR token-decoder, the sound generation time increases linearly with the sound duration. To overcome the shortcomings introduced by AR token-decoders, we propose a non-autoregressive token-decoder based on the discrete diffusion model, named Diffsound. Specifically, the Diffsound model predicts all of the mel-spectrogram tokens in one step and then refines the predicted tokens in the next step, so the best-predicted results can be obtained by iteration. Our experiments show that our proposed Diffsound model not only produces better text-to-sound generation results when compared with the AR token-decoder but also has a faster generation speed, i.e. , MOS: 3.56 v.s 2.786, and the generation speed is five times faster than the AR decoder. Furthermore, to automatically assess the quality of generated samples, we define three different objective evaluation metrics i.e. , Fréchet Inception Distance (FID), Kullback-Leibler (KL), and audio caption loss, which can comprehensively assess the relevance and fidelity of the generated samples.

The advancement of audio-language (AL) multimodal learning tasks has been significant in recent years, yet the limited size of existing audio-language datasets poses challenges for researchers due to the costly and time-consuming collection process. To address this data scarcity issue, we introduce WavCaps, the first large-scale weakly-labelled audio captioning dataset, comprising approximately 400k audio clips with paired captions. We sourced audio clips and their raw descriptions from web sources and a sound event detection dataset. However, the online-harvested raw descriptions are highly noisy and unsuitable for direct use in tasks such as automated audio captioning. To overcome this issue, we propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT, a large language model, is leveraged to filter and transform raw descriptions automatically. We conduct a comprehensive analysis of the characteristics of WavCaps dataset and evaluate it on multiple downstream audio-language multimodal learning tasks. The systems trained on WavCaps outperform previous state-of-the-art (SOTA) models by a significant margin. Our aspiration is for the WavCaps dataset we have proposed to facilitate research in audio-language multimodal learning and demonstrate the potential of utilizing large language models (LLMs) to enhance academic research. Our dataset and codes are available at https://github.com/XinhaoMei/WavCaps.

Target speech separation refers to extracting a target speaker's voice from an overlapped audio of simultaneous talkers. Previously the use of visual modality for target speech separation has demonstrated great potentials. This work proposes a general multi-modal framework for target speech separation by utilizing all the available information of the target speaker, including his/her spatial location, voice characteristics and lip movements. Also, under this framework, we investigate on the fusion methods for multi-modal joint modeling. A factorized attention-based fusion method is proposed to aggregate the high-level semantic information of multi-modalities at embedding level. This method firstly factorizes the mixture audio into a set of acoustic subspaces, then leverages the target's information from other modalities to enhance these subspace acoustic embeddings with a learnable attention scheme. To validate the robustness of proposed multi-modal separation model in practical scenarios, the system was evaluated under the condition that one of the modalities is temporarily missing, invalid or corrupted. Experiments are conducted on a large-scale audio-visual dataset collected from YouTube (to be released) that spatialized by simulated room impulse responses (RIRs). Experiment results illustrate that our proposed multi-modal framework significantly outperforms single-modal and bi-modal speech separation approaches, while can still support real-time processing.

Concepts, a collective term for meaningful words that correspond to objects, actions, and attributes, can act as an intermediary for video captioning. While many efforts have been made to augment video captioning with concepts, most methods suffer from limited precision of concept detection and insufficient utilization of concepts, which could provide caption generation with inaccurate and inadequate prior information. Considering these issues, we propose a Concept-awARE video captioning framework (CARE) to facilitate plausible caption generation. Based on the encoder-decoder structure, CARE detects concepts precisely via multimodal-driven concept detection (MCD) and offers sufficient prior information to caption generation by global-local semantic guidance (G-LSG). Specifically, we implement MCD by leveraging video-to-text retrieval and the multimedia nature of videos. To achieve G-LSG, given the concept probabilities predicted by MCD, we weight and aggregate concepts to mine the video’s latent topic to affect decoding globally and devise a simple yet efficient hybrid attention module to exploit concepts and video content to impact decoding locally. Finally, to develop CARE, we emphasize on the knowledge transfer of a contrastive vision-language pre-trained model (i.e., CLIP) in terms of visual understanding and video-to-text retrieval. With the multi-role CLIP, CARE can outperform CLIP-based strong video captioning baselines with affordable extra parameter and inference latency costs. Extensive experiments on MSVD, MSR-VTT, and VATEX datasets demonstrate the versatility of our approach for different encoder-decoder networks and the superiority of CARE against state-of-the-art methods. Our code is available at https://github.com/yangbang18/CARE.

Automatic radiology report generation has attracted enormous research interest due to its practical value in reducing the workload of radiologists. However, simultaneously establishing global correspondences between the image (e.g., Chest X-ray) and its related report and local alignments between image patches and keywords remains challenging. To this end, we propose an Unify, Align and then Refine (UAR) approach to learn multi-level cross-modal alignments and introduce three novel modules: Latent Space Unifier (LSU), Cross-modal Representation Aligner (CRA) and Text-to-Image Refiner (TIR). Specifically, LSU unifies multimodal data into discrete tokens, making it flexible to learn common knowledge among modalities with a shared network. The modality-agnostic CRA learns discriminative features via a set of orthonormal basis and a dual-gate mechanism first and then globally aligns visual and textual representations under a triplet contrastive loss. TIR boosts token-level local alignment via calibrating text-to-image attention with a learnable mask. Additionally, we design a two-stage training procedure to make UAR gradually grasp cross-modal alignments at different levels, which imitates radiologists’ workflow: writing sentence by sentence first and then checking word by word. Extensive experiments and analyses on IU-Xray and MIMIC-CXR benchmark datasets demonstrate the superiority of our UAR against varied state-of-the-art methods.