SELD-TCN: Sound Event Localization & Detection via Temporal Convolutional Networks

The understanding of the surrounding environment plays a critical role in autonomous robotic systems, such as self-driving cars. Extensive research has been carried out concerning visual perception. Yet, to obtain a more complete perception of the environment, autonomous systems of the future should also take acoustic information into account. Recent sound event localization and detection (SELD) frameworks utilize convolutional recurrent neural networks (CRNNs). However, considering the recurrent nature of CRNNs, it becomes challenging to implement them efficiently on embedded hardware. Not only are their computations strenuous to parallelize, but they also require high memory bandwidth and large memory buffers. In this work, we develop a more robust and hardware-friendly novel architecture based on a temporal convolutional network(TCN). The proposed framework (SELD-TCN) outperforms the state-of-the-art SELDnet performance on four different datasets. Moreover, SELD-TCN achieves 4x faster training time per epoch and 40x faster inference time on an ordinary graphics processing unit (GPU).Comment: 5 pages, 3 tables, 2 figures. Submitted to EUSIPCO 202

Automatic Audio Indexing and Audio Playback Speed Control as Tools for Language Learning

Abstract. The Gong system has been developed for web based communication. It supports synchronous and asynchronous audio communication and can be embedded in other learning management systems. This paper discusses two novel features which are targeted at language learners using the system. The first is the ability to automatically index an audio recording. After the indexing has taken place the user is able to select one or several words and hear just those words spoken in isolation. The second is the ability to vary the playback speed of any recorded message. The technical details of their implementation as well as pedagogical use of these features are discussed