Ensemble of pruned models for low-complexity acoustic scene classification

For the DCASE 2020 Challenge, the focus of Task 1B is to develop low-complexity models for classification of 3 different types of acoustic scenes, which have potential applications in resource-scarce edge devices deployed in a large-scale acoustic network. In this paper, we present the training methodology for our submissions for the challenge, with the best-performing system consisting of an ensemble of VGGNet- and Inception-Net-based lightweight classification models. The subsystems in the ensemble classifier were pruned by setting low-magnitude weights periodically to zero with a polynomial decay schedule to achieve an 80% reduction in individual subsystem size. The resultant ensemble classifier outperformed the baseline model on the validation set over 10 runs and had 119758 non-zero parameters taking up 468KB of memory. This shows the efficacy of the pruning technique used. We also performed experiments to compare the performance of various data augmentation schemes, input feature representations, and model architectures in our training methodology. No external data was used, and source code for the submission can be found at https://github.com/kenowr/DCASE-2020-Task-1B.Ministry of Education (MOE)Accepted versionSupported by the Singapore Ministry of Education Academic Research Fund Tier-2, under research grant MOE2017-T2-2-060

Implementing continuous HRTF measurement in near-field

Head-related transfer function (HRTF) is an essential component to create an immersive listening experience over headphones for virtual reality (VR) and augmented reality (AR) applications. Metaverse combines VR and AR to create immersive digital experiences, and users are very likely to interact with virtual objects in the near-field (NF). The HRTFs of such objects are highly individualized and dependent on directions and distances. Hence, a significant number of HRTF measurements at different distances in the NF would be needed. Using conventional static stop-and-go HRTF measurement methods to acquire these measurements would be time-consuming and tedious for human listeners. In this paper, we propose a continuous measurement system targeted for the NF, and efficiently capturing HRTFs in the horizontal plane within 45 secs. Comparative experiments are performed on head and torso similar (HATS) and human listeners to evaluate system consistency and robustness.Ministry of Education (MOE)Submitted/Accepted versionThis research is supported by the Singapore Ministry of Education, Academic Research Fund Tier 2, under research grant MOE-T2EP20221-0014