Signal processing techniques for blind source separation

所要解决的问题在信号处理领域中是众所周知的，并且迄今为止已经通过能够隔离在信号混合中线性组合的多个不同信号的算法技术的开发来解决。在这一领域已经有了大量的工作，并有一些相当成熟的技术，例如基于非负矩阵分解和独立分量分析。然而，目前的一个硕士项目揭示了大量尚未得到满意解决的问题。这些问题包括陷入局部最小值的优化算法的性能不佳和恢复信号相位的重建不令人满意。此外，最近在基于应用深度神经网络来提高源分离性能的方法方面有所发展。迄今为止的结果显示出显着的潜力时，应用到相对简单的任务，如分离的歌声或单一的乐器。关于多通道分离的文献较少，因为问题变得更加复杂，并且必须考虑额外的空间信息。虽然这个项目将有很大的潜力比较，发展，并改善现有的方法，更一般的问题，具体的工作重点将是发展的技术，可以结合联合收割机源分离与虚拟声学演示。因此，这项工作将与迄今为止在EPSRC“S3A”方案赠款方面开展的工作高度互补，该奖学金是通过该方案赠款供资的。

The problem to be addressed is well known in the field of signal processing and to date has been tackled through the development of algorithmic techniques capable of isolating a number of different signals combined linearly in a mixture of signals. There is already a significant body of work in this field with some fairly well developed techniques, based on, for example, non-negative matrix factorisation and independent component analysis. However, a current MSc project has revealed a significant number of issues that have yet to be satisfactorily resolved. These include the poor performance of optimisation algorithms that become trapped in local minima and the unsatisfactory reconstruction of recovered signal phase. Furthermore, there have been developments more recently in approaches based on the application of deep neural networks to increase the performance of source separation. Results presented to date show significant potential when applied to relatively simple tasks such as the separation of a singing voice or a single instrument. Less literature exists for multichannel separation, since the problem becomes more complex and additional spatial information has to be taken into account. Whilst this project will have plenty of potential for comparing, developing, and improving existing approaches to the more general problem, the specific focus of the work will be will on the development of techniques that can combine source separation with virtual acoustic presentation. The work will thus be highly complementary to the work undertaken to date on the EPSRC "S3A" programme grant through which this studentship is funded.