Anurag Agarwal
Reader in Acoustics and Biomedical Technology
Anurag is the head of the Acoustics lab in the Engineering Department at the University of Cambridge. He is a Reader at the University of Cambridge, a Fellow at Emmanuel College and the CEO of BioPhonics Limited. His research interests are in the field of acoustics and aerodynamics of aerospace, domestic appliances and biomedical applications. His collaborators include Rolls-Royce, General Electric, Boeing, Mitsubishi Heavy Industries, JCB, Dyson, Addenbrookes and Papworth Hospitals.
Alastair Gregory
Junior Research Fellow
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.
Ed Kay
PhD student
Ed is working on understanding and classifying heart sounds. He is producing physical models to understand the causes of the heart murmurs associated with aortic stenosis and mitral regurgitation. He is also using machine learning techniques to try and classify heart sounds as either normal or abnormal.
Max Nussbaumer
PhD student
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.
Oscar Wilsby
PhD student
Oscar is developing 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 is working to implement low order models that can be used to assess noise levels early in the design process. This work involves using analytical models together with computationally demanding fluid dynamics simulations to devise quick but accurate methods for noise production. Oscar is also interested in utilising machine learning algorithms in optimisation for low noise design.
Andrew McDonald
PhD student
Andrew is developing machine learning techniques to diagnose cardiovascular disease from heart sounds. He is working with Ed to develop an intelligent stethoscope, which is capable of reliable screening 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 Lamarquette
PhD student
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 already gave 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.
