Interactive demonstrations in acoustic signal processing using Jupyter notebooks

Abstract

In studying acoustic signal processing, students are faced with seemingly abstract concepts such as continuous-time and discrete-time representations, time-frequency transformations, convolution, vector and inner product spaces, orthogonality, and optimal and adaptive filtering. While mathematical development of the theory and illustration through simple examples during teaching can help shed light on some of these concepts, they are not always sufficient for students to achieve a full level of understanding. As a means to improve this situation, we have consequently developed several interactive demonstrations in acoustic signal processing, covering a range of topics, from the fundamentals of sampling and quantisation to the more advanced optimal and adaptive filtering. These demonstrations have been made with Jupyter notebooks and offer several advantages. Apart from being open source, students immediately get to work with realistic data as they can generate and record acoustic signals using their computer’s loudspeaker and microphone. Using the live coding aspect of the notebooks, they can also quickly process, visualise, and listen to the results from the processing of these acoustic signals, as well as repeat this workflow for various parameter sweeps. We will present some of these demonstrations and show how they can serve as an effective teaching aid.