Intelligent Analysis of Composite Acoustic Signals (Computer and Information Science)

这项研究涉及机器感知和 分析 复杂的声音信号。 目标是可靠地识别和 跟踪单声道信号中的同时声源。 在 建议的架构，感知结果的相互作用， 数据驱动和期望驱动的代理人。 配置 系统代理的资源以及反馈循环的控制 不同层次的解释之间的时变 用模拟实时策略来处理信号 解决问题 在最低层次的分析中， 信号处理，用于与焦点切换的叠加 光学，应该同时产生高分辨率 和频率，从而提供了一种有效的方法， 传统的带宽-时间权衡。 源一致性准则 源自听觉流的心理声学工作（包括 分音之间的相关AM和FM调制）预计将 当更熟悉的方法无法分离源时， 还不够 所提出的系统依赖于学习协处理器， 使自己适应一个信号越来越难以捉摸的方面。 相关技术包括传统的参数自适应， 数值分类法、句法模式匹配和概念 学习尤其重要的是混合动力的发展 组合参数化视图和结构视图的联合收割机方法。 在 总之，这项研究涉及声学分析的关键领域， 以及人工智能体系结构中更广泛的问题， 智能

This research is concerned with machine perception and analysis of complex sound signals. The goaL is to reliably identify and track simultaneous acoustic sources in a monaural signal. In the proposed architecture, perception results from the interaction of data-driven and expectation-driven agents. The allocation of resources to systems agents, and the control of feedback loops between different levels of interpretation of the time-varying signal are approached with strategies that simulate real-time problem solving. At the lowest level of analysis, multi-rate signal processing, used in conjection with focus-switching heuristics, ought to yield high resolution simultaneously in time and frequency, thus giving an efficient method for improving upon traditional bandwidth-time tradeoffs. Source coherence criteria derived from psychoacoustic work on auditory streaming (including correlated AM and FM modulation among partials) are expected to be useful for separating sources when more familiar methods do not suffice. The proposed system relies on a learning co-processor to attune itself to increasingly elusive aspects of a signal. Relevant techniques include traditional parameter adaptation, numerical taxonomy, syntatic pattern matching and concept learning. Especially important is the development of hybrid methods that combine parametric and structural views. In summary, the research addresses key areas of acoustic analysis as well as broader issues in the architecture of artificial intelligence.