arXiv subjects

Woon-Seng Gan

Publications and source records attributed to Woon-Seng Gan.

76 records · Page 5Linked to original sources

arXiv2021

A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context

This paper introduces SINGA:PURA, a strongly labelled polyphonic urban sound dataset with spatiotemporal context. The data were collected via several recording units deployed across Singapore as a part of a wireless acoustic sensor network. These recordings were made as part of a project to identify and mitigate noise sources in Singapore, but also possess a wider applicability to sound event detection, classification, and localization. This paper introduces an accompanying hierarchical label taxonomy, which has been designed to be compatible with other existing datasets for urban sound tagging while also able to capture sound events unique to the Singaporean context. This paper details the data collection, annotation, and processing methodologies for the creation of the dataset. We further perform exploratory data analysis and include the performance of a baseline model on the dataset as a benchmark.

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arXiv2021

Design and Evaluation of Active Noise Control on Machinery Noise

Construction workers and residents live near around construction sites are exposed to noises that might cause hearing loss, high blood pressure, heart disease, sleep disturbance and stress. Regulations has been carried out by national governments to limit the maximum permissible noise levels for construction works. A four-channel active noise control system mounted on the opening of an enclosure is designed to prevent the machinery noise from spreading around and retaining the heat diffusion path. Multi-channel FxLMS algorithm in time domain is implemented on the main controller. A Genelec speaker is placed inside the box as the primary noise source to play back different types of noises. Analyses and experiments are carried out to investigate the controllable frequency range of this ANC system in detail. Considerable noise reduction performance is achieved for different recorded practical construction noises.

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arXiv2020

A General Network Architecture for Sound Event Localization and Detection Using Transfer Learning and Recurrent Neural Network

Polyphonic sound event detection and localization (SELD) task is challenging because it is difficult to jointly optimize sound event detection (SED) and direction-of-arrival (DOA) estimation in the same network. We propose a general network architecture for SELD in which the SELD network comprises sub-networks that are pretrained to solve SED and DOA estimation independently, and a recurrent layer that combines the SED and DOA estimation outputs into SELD outputs. The recurrent layer does the alignment between the sound classes and DOAs of sound events while being unaware of how these outputs are produced by the upstream SED and DOA estimation algorithms. This simple network architecture is compatible with different existing SED and DOA estimation algorithms. It is highly practical since the sub-networks can be improved independently. The experimental results using the DCASE 2020 SELD dataset show that the performances of our proposed network architecture using different SED and DOA estimation algorithms and different audio formats are competitive with other state-of-the-art SELD algorithms. The source code for the proposed SELD network architecture is available at Github.

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arXiv2020

A Sequence Matching Network for Polyphonic Sound Event Localization and Detection

Polyphonic sound event detection and direction-of-arrival estimation require different input features from audio signals. While sound event detection mainly relies on time-frequency patterns, direction-of-arrival estimation relies on magnitude or phase differences between microphones. Previous approaches use the same input features for sound event detection and direction-of-arrival estimation, and train the two tasks jointly or in a two-stage transfer-learning manner. We propose a two-step approach that decouples the learning of the sound event detection and directional-of-arrival estimation systems. In the first step, we detect the sound events and estimate the directions-of-arrival separately to optimize the performance of each system. In the second step, we train a deep neural network to match the two output sequences of the event detector and the direction-of-arrival estimator. This modular and hierarchical approach allows the flexibility in the system design, and increase the performance of the whole sound event localization and detection system. The experimental results using the DCASE 2019 sound event localization and detection dataset show an improved performance compared to the previous state-of-the-art solutions.

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