This paper presents a video-to-audio generation framework, VTA-LDM, which generates semantically and temporally aligned audio content based on video input. The framework is built upon a latent diffusion model (LDM) with encoded vision features as the generation condition. The model uses a vision encoder to extract and interpret visual features from the input video, capturing complex visual patterns essential for generating relevant audio content. Auxiliary embeddings, such as textual descriptions and positional embeddings, are also incorporated to provide additional contextual information. Data augmentation techniques are used to improve the model's generalization capabilities. The framework is evaluated on the VGGSound dataset, which contains over 550 hours of videos with acoustic visual-audio event pairs. The model is trained on 200k videos and validated on 3k videos. The evaluation focuses on semantic alignment and temporal alignment, with results showing that the model achieves state-of-the-art performance in generating high-quality, diverse, and temporally-aligned audios. The study also explores the impact of different vision encoders, auxiliary embeddings, and data augmentation techniques on the model's performance. The results demonstrate that the model outperforms existing baselines in audio generation, particularly in terms of semantic and temporal alignment. The study also shows that incorporating additional features, such as extra textual prompts and positional embeddings, can significantly enhance the generation quality and synchronization between audio and visual elements. The findings indicate that the overall framework empowers the model to learn and comprehend the dynamics of the scene, demonstrating the potential for further exploration and refinement in the video-to-audio generation domain. The paper concludes that the proposed framework has the potential to advance the development of more realistic and accurate audio-visual generation models.This paper presents a video-to-audio generation framework, VTA-LDM, which generates semantically and temporally aligned audio content based on video input. The framework is built upon a latent diffusion model (LDM) with encoded vision features as the generation condition. The model uses a vision encoder to extract and interpret visual features from the input video, capturing complex visual patterns essential for generating relevant audio content. Auxiliary embeddings, such as textual descriptions and positional embeddings, are also incorporated to provide additional contextual information. Data augmentation techniques are used to improve the model's generalization capabilities. The framework is evaluated on the VGGSound dataset, which contains over 550 hours of videos with acoustic visual-audio event pairs. The model is trained on 200k videos and validated on 3k videos. The evaluation focuses on semantic alignment and temporal alignment, with results showing that the model achieves state-of-the-art performance in generating high-quality, diverse, and temporally-aligned audios. The study also explores the impact of different vision encoders, auxiliary embeddings, and data augmentation techniques on the model's performance. The results demonstrate that the model outperforms existing baselines in audio generation, particularly in terms of semantic and temporal alignment. The study also shows that incorporating additional features, such as extra textual prompts and positional embeddings, can significantly enhance the generation quality and synchronization between audio and visual elements. The findings indicate that the overall framework empowers the model to learn and comprehend the dynamics of the scene, demonstrating the potential for further exploration and refinement in the video-to-audio generation domain. The paper concludes that the proposed framework has the potential to advance the development of more realistic and accurate audio-visual generation models.