results: 该算法在多种popular编程语言中的实现可以免除听觉switching artifacts，并且可以保持常见计算成本和内存占用量。代码可以在GitHub上免费下载。Abstract
Virtual and augmented realities are increasingly popular tools in many domains such as architecture, production, training and education, (psycho)therapy, gaming, and others. For a convincing rendering of sound in virtual and augmented environments, audio signals must be convolved in real-time with impulse responses that change from one moment in time to another. Key requirements for the implementation of such time-variant real-time convolution algorithms are short latencies, moderate computational cost and memory footprint, and no perceptible switching artifacts. In this engineering report, we introduce a partitioned convolution algorithm that is able to quickly switch between impulse responses without introducing perceptible artifacts, while maintaining a constant computational load and low memory usage. Implementations in several popular programming languages are freely available via GitHub.
摘要
虚拟和增强现实技术在多个领域得到了广泛应用，如建筑、生产、培训和教育、心理治疗、游戏等。为在虚拟和增强环境中提供真实的声音渲染，音频信号需要在实时中扩散到不同的冲击回应函数，这些函数在时间上变化。实现时变实时扩散算法的关键要求包括：短延迟时间、moderate计算成本和内存占用量，无法识别的切换 artifacts。本工程报告中，我们介绍了一种分解扩散算法，可以快速切换冲击回应函数，而无需引入明显的artefacts，同时保持了常量计算负担和内存占用量。实现在多种popular编程语言上可以免费获取于GitHub。
A Novel U-Net Architecture for Denoising of Real-world Noise Corrupted Phonocardiogram Signal
For: 本研究旨在提出一种基于U-Net深度神经网络架构的心听音信号杂谔除去方法，以解决在医学 auscultation 中心听音信号杂谔问题。* Methods: 为了设计、开发和验证提议的架构，我们提出了一种新的实验方法，利用现实世界噪声污染的PCG信号 DATASET 和一个开放式PCG DATASET。* Results: 对比与现有状态的先进技术，我们的杂谔除去方法在Synthesized noisy PCG DATASET 上的性能评估表明，提出的方法在识别和预测方面具有显著的改进。Abstract
The bio-acoustic information contained within heart sound signals are utilized by physicians world-wide for auscultation purpose. However, the heart sounds are inherently susceptible to noise contamination. Various sources of noises like lung sound, coughing, sneezing, and other background noises are involved in such contamination. Such corruption of the heart sound signal often leads to inconclusive or false diagnosis. To address this issue, we have proposed a novel U-Net based deep neural network architecture for denoising of phonocardiogram (PCG) signal in this paper. For the design, development and validation of the proposed architecture, a novel approach of synthesizing real-world noise corrupted PCG signals have been proposed. For the purpose, an open-access real-world noise sample dataset and an open-access PCG dataset has been utilized. The performance of the proposed denoising methodology has been evaluated on the synthesized noisy PCG dataset. The performance of the proposed algorithm has been compared with existing state-of-the-art (SoA) denoising algorithms qualitatively and quantitatively. The proposed denoising technique has shown improvement in performance as comparison to the SoAs.
摘要
生物声学信息在心声信号中含有，医生世界各地通过 auscultation 来利用这些信息。然而，心声信号具有自然潜在的噪声污染。这些噪声包括肺 зву、喷嚏、喷嚏、和其他背景噪声等。这种噪声污染可能导致不正确或不 conclution 的诊断。为解决这个问题，我们在本文中提出了一种基于 U-Net 深度神经网络架构的PCG 信号杂音除除法。为了设计、开发和验证该架构，我们提出了一种新的实际噪声污染 PCG 信号生成方法。为此，我们使用了一个开放访问的实际噪声样本数据集和一个开放访问的 PCG 数据集。我们对提出的杂音除法方法进行评估，并与现有状态的最佳方法（SoA）进行比较。我们发现，提出的杂音除法方法在比较 SoA 方法时显示出了改善的性能。