Machine learning algorithms for analyzing auditory scenes with multiple sound sources

用于分析具有多个声源的听觉场景的机器学习算法

• 批准号: 0535251
• 负责人: Terrence Sejnowski
• 金额: --
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0535251&HistoricalAwards=false
• 关键词: Machine; learning; algorithms; analyzing; auditory

Computer algorithms that analyze auditory scenes and extract individual sound sources would have a strong impact in several domains. For example, to facilitate natural interaction with computing devices by voice, an automatic speech recognition system must be able to focus on the voice of the person speaking to it and ignore sounds from all other sources. A hearing device must perform a similar task to allow a hearing impaired person conduct a conversation in a noisy, multiple source environment. Building on recent advances in the fields of machine learning and signal processing, we are developing sophisticated adaptive algorithms for analyzing auditory scenes with multiple sound sources. Our algorithms are based on probabilistic modeling of different sound sources and of the manner in which they overlap each other and distorted by reverberation and background noise. We use advanced recent techniques for inferring our models from sound data captured by a microphone array, separating those data into individual sources, and automatically determining the type of each source present and its location. Moreover, by reconstructing the clean signal of individual sound sources, we dramatically enhance the accuracy of automatic speech recognition for human speakers in multiple source environments. To facilitate the development and evaluation of our algorithms, and also to encourage competition between other research groups ultimately resulting in improved techniques, we collect a large dataset of multiple source auditory scenes, and make it publicly available on a dedicated website.

分析听觉场景并提取单个声源的计算机算法将在多个领域产生重大影响。例如，为了促进通过语音与计算设备的自然交互，自动语音识别系统必须能够专注于对它说话的人的语音，而忽略来自所有其他来源的声音。听力设备必须执行类似的任务，以允许听力受损的人在嘈杂的多源环境中进行对话。基于机器学习和信号处理领域的最新进展，我们正在开发复杂的自适应算法，用于分析具有多个声源的听觉场景。我们的算法是基于不同的声源的概率建模和它们相互重叠的方式，并通过混响和背景噪声失真。我们使用先进的最新技术从麦克风阵列捕获的声音数据中推断我们的模型，将这些数据分离为单个源，并自动确定每个源的类型及其位置。此外，通过重构单个声源的干净信号，我们大大提高了多源环境中说话人自动语音识别的准确性。为了促进我们算法的开发和评估，也为了鼓励其他研究小组之间的竞争，最终导致技术的改进，我们收集了一个大型的多源听觉场景数据集，并在一个专门的网站上公开提供。