Alastair is the Neville Junior Research Fellow at Magdalene College, and has worked in the acoustics lab since 2014. His early work developed a new kind of transformation to allow better understanding of how sound propagates in the presence of background flow, using analogies between aeroacoustics and relativity. He then moved into biomedical applications. His PhD developed a model for the mechanism behind wheezing sounds, producing a simple relationship between wheezing frequency and the tube material properties and geometry. This can be used by clinicians to learn about changes in stiffness of lung tissue and the position of blockages and stenoses. He is now expanding understanding of other sounds made in the lung, such as crackles, as well as more general bodily sounds that can be used for diagnosis. In his spare time he has also been building a wooden sailing dingy in the Dyson Centre at the Engineering Department.

Max is working on understanding sound propagation through the human chest. The aim is to develop a method of generating acoustic maps of the chest that can help clinicians diagnose certain diseases. Max is working with Alastair on the development of a microphone array that will be used for sound localisation in the chest. In his previous work with the group he has studied the aeroacoustics of free reeds.

Andrew is developing machine learning techniques to diagnose cardiovascular disease from heart sounds. He is developing an intelligent stethoscope, which is capable of reliable detection of valvular heart disease. The device will help clinicians detect heart problems earlier and more accurately, to improve patient prognoses and reduce unnecessary referrals to cardiologists.

Amélie is studying brain aneurysms by trying to understand the fundamental mechanisms behind the generation of their noise. Her current work involves experimental investigations which have already given very encouraging preliminary results. The final aim of her project is to develop a novel non-invasive device for early detection of aneurysms. As ruptured aneurysms carry a high mortality rate, an early diagnosis permits better management of the lesion which at a larger scale could save several lives.

Ed worked on understanding and classifying heart sounds. He produced physical models to understand the causes of the heart murmurs associated with aortic stenosis and mitral regurgitation. He also used machine learning techniques to classify heart sounds as either normal or abnormal.

Oscar developed noise prediction methods for turbomachinery operating at low Reynolds number. Noise reduction of air-moving devices such as axial compressors is becoming increasingly important for the industrial engineer, as stringent regulations are placing acoustic design on near equal terms with aerodynamic efficiency. Consequently, noise can no longer be accepted as an undesirable by-product, but rather must be accounted for at an earlier stage, ideally in tandem with aerodynamic design. Oscar worked to implement low order models that can be used to assess noise levels early in the design process. This work involved using analytical models together with computationally demanding fluid dynamics simulations to devise quick but accurate methods for noise production. Oscar was also interested in utilising machine learning algorithms in optimisation for low noise design.