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With rapid urbanization comes the increase of community, construction, and transportation noise in residential areas. The conventional approach of solely relying on sound pressure level (SPL) information to decide on the noise environment and to plan out noise control and mitigation strategies is inadequate. This paper presents an end-to-end IoT system that extracts real-time urban sound metadata using edge devices, providing information on the sound type, location and duration, rate of occurrence, loudness, and azimuth of a dominant noise in nine residential areas. The collected metadata on environmental sound is transmitted to and aggregated in a cloud-based platform to produce detailed descriptive analytics and visualization. Our approach to integrating different building blocks, namely, hardware, software, cloud technologies, and signal processing algorithms to form our real-time IoT system is outlined. We demonstrate how some of the sound metadata extracted by our system are used to provide insights into the noise in residential areas. A scalable workflow to collect and prepare audio recordings from nine residential areas to construct our urban sound dataset for training and evaluating a location-agnostic model is discussed. Some practical challenges of managing and maintaining a sensor network deployed at numerous locations are also addressed., Comment: 13 pages, 15 figures, journal

This technical report details our systems submitted for Task 3 of the DCASE 2024 Challenge: Audio and Audiovisual Sound Event Localization and Detection (SELD) with Source Distance Estimation (SDE). We address only the audio-only SELD with SDE (SELDDE) task in this report. We propose to improve the existing ResNet-Conformer architectures with Squeeze-and-Excitation blocks in order to introduce additional forms of channel- and spatial-wise attention. In order to improve SELD performance, we also utilize the Spatial Cue-Augmented Log-Spectrogram (SALSA) features over the commonly used log-mel spectra features for polyphonic SELD. We complement the existing Sony-TAu Realistic Spatial Soundscapes 2023 (STARSS23) dataset with the audio channel swapping technique and synthesize additional data using the SpatialScaper generator. We also perform distance scaling in order to prevent large distance errors from contributing more towards the loss function. Finally, we evaluate our approach on the evaluation subset of the STARSS23 dataset., Comment: Technical report for DCASE 2024 Challenge Task 3

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 simulator (HATS) and human listeners to evaluate system consistency and robustness., Comment: 5 pages, 9 figures, Submitted to 2023 IEEE International Conference on Acoustics, Speech and Signal Processing