Robust signal analysis and recognition techiques for man-machine interaction

新的应用已经被开发出来，比如自动语音翻译、互联网搜索工具、多模式计算机交互。这些系统不能充分处理被破坏的、有噪声的信号。他们还使用模式识别技术，这需要在培训期间进行监督。我提出了稳健的信号分析技术，并开发了生物启发和无监督模式识别器。拟议的研究结果可以用来帮助创建能够与环境互动的新的智能系统。神经电生理学也可以受益于将为这项研究开发的新的信号分析和识别工具。我将致力于开发声源分离和智能信号识别方面的新系统，这些系统具有无人监督的能力，并在有限的数据集上进行培训。当前最先进的统计系统通常使用具有大量数据的监督训练。因此，这种系统的设计和培训是繁重和非常昂贵的。棘波的动态、非线性、空间和时间过滤与神经递质的扩散以及神经元的拓扑组织相结合，产生了同时的信号处理和识别。此外，尖峰技术允许将信息编码成与平时不同的第二个时间尺度。这第二个时间尺度编码了神经元放电的相对时间。这是一个允许无人监督培训的关键功能。同步或生成特定的尖峰时间序列成为自动组织标准。在长期的时间尺度(5年)上，我建议探索两个领域的整合和相互作用：传统的信号和模式识别范式(分析和识别之间的差异)和外周神经系统进行的连续统一体(在组织上)和动态(在时间上)分析/识别，重点是信号处理。在较短的时间范围内(每个项目2-3年)，我将专注于源分离、信号和语音识别等应用程序。

New applications have been developed like automatic speech translation, internet search tools, multi-modal computer interactions. These systems can not adequately process corrupted, noisy signals. They also use pattern recognition techniques that require supervision during training. I propose robust signal analysis techniques and to develop bio-inspired and unsupervised pattern recognizers. Results of the proposed research can be used  to assist in the creation of new intelligent systems that can interact with the environment. Electroneurophysiology can also benefit from the new signal analysis and recognition tools that will be developed for this research. I will work on the development of new systems in sound source separation and intelligent signal recognition with capabilities of being unsupervised and trained on limited data sets. Current state of the art statistical systems usually use supervised training with very much data. Therefore, the design and training of such systems is heavy and very costly. The dynamical, non linear, space and time filtering of spikes combined with neurotransmitters diffusion  along with the topographic organization of neurones yields simultaneous signal processing and recognition. Moreover, spiking allows the encoding of information into a second time scale that is different from the usual time. This second time scale encodes the relative timing of firing neurones. It is a key feature that allows unsupervised training. Synchronization or the generation of specific spiking temporal sequences becomes an auto-organization criteria. On a long term time scale (5 years), I propose to explore the integration and interaction of two fields: the conventional signal and pattern recognition paradigm (discrepancy between analysis and recognition) and the continuum (in organization) and dynamical (in times) analysis/recognition made by the peripheral nervous system with emphasis on signal processing. On a shorter time scale  (2--3 years per project), I will focus on applications like sources separation, signal and speech recognition.