arXiv subjects

Woon-Seng Gan

Publications and source records attributed to Woon-Seng Gan.

At least 37 records · Page 2Linked to original sources

arXiv2025

A Robust Proactive Communication Strategy for Distributed Active Noise Control Systems

Distributed multichannel active noise control (DMCANC) systems assign the high computational load of conventional centralized algorithms across multiple processing nodes, leveraging inter-node communication to collaboratively suppress unwanted noise. However, communication overhead can undermine algorithmic stability and degrade overall performance. To address this challenge, we propose a robust communication framework that integrates adaptive-fixed-filter switching and the mixed-gradient combination strategy. In this approach, each node independently executes a single-channel filtered reference least mean square (FxLMS) algorithm while monitoring real-time noise reduction levels. When the current noise reduction performance degrades compared to the previous state, the node halts its adaptive algorithm, switches to a fixed filter, and simultaneously initiates a communication request. The exchanged information comprises the difference between the current control filter and the filter at the time of the last communication, equivalent to the accumulated gradient sum during non-communication intervals. Upon receiving neighboring cumulative gradients, the node employs a mixed-gradient combination method to update its control filter, subsequently reverting to the adaptive mode. This proactive communication strategy and adaptive-fixed switching mechanism ensure system robustness by mitigating instability risks caused by communication issues. Simulations demonstrate that the proposed method achieves noise reduction performance comparable to centralized algorithms while maintaining stability under communication constraints, highlighting its practical applicability in real-world distributed ANC scenarios.

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arXiv2025

Acoustic Scene Classification Using CNN-GRU Model Without Knowledge Distillation

In this technical report, we present the SNTL-NTU team's Task 1 submission for the Low-Complexity Acoustic Scenes and Events (DCASE) 2025 challenge. This submission departs from the typical application of knowledge distillation from a teacher to a student model, aiming to achieve high performance with limited complexity. The proposed model is based on a CNN-GRU model and is trained solely using the TAU Urban Acoustic Scene 2022 Mobile development dataset, without utilizing any external datasets, except for MicIRP, which is used for device impulse response (DIR) augmentation. The proposed model has a memory usage of 114.2KB and requires 10.9M muliply-and-accumulate (MAC) operations. Using the development dataset, the proposed model achieved an accuracy of 60.25%.

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arXiv2025

Joint Feature and Output Distillation for Low-complexity Acoustic Scene Classification

This report presents a dual-level knowledge distillation framework with multi-teacher guidance for low-complexity acoustic scene classification (ASC) in DCASE2025 Task 1. We propose a distillation strategy that jointly transfers both soft logits and intermediate feature representations. Specifically, we pre-trained PaSST and CP-ResNet models as teacher models. Logits from teachers are averaged to generate soft targets, while one CP-ResNet is selected for feature-level distillation. This enables the compact student model (CP-Mobile) to capture both semantic distribution and structural information from teacher guidance. Experiments on the TAU Urban Acoustic Scenes 2022 Mobile dataset (development set) demonstrate that our submitted systems achieve up to 59.30\% accuracy.

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arXiv2025

Self-Boosted Weight-Constrained FxLMS: A Robustness Distributed Active Noise Control Algorithm Without Internode Communication

Compared to the conventional centralized multichannel active noise control (MCANC) algorithm, which requires substantial computational resources, decentralized approaches exhibit higher computational efficiency but typically result in inferior noise reduction performance. To enhance performance, distributed ANC methods have been introduced, enabling information exchange among ANC nodes; however, the resulting communication latency often compromises system stability. To overcome these limitations, we propose a self-boosted weight-constrained filtered-reference least mean square (SB-WCFxLMS) algorithm for the distributed MCANC system without internode communication. The WCFxLMS algorithm is specifically designed to mitigate divergence issues caused by the internode cross-talk effect. The self-boosted strategy lets each ANC node independently adapt its constraint parameters based on its local noise reduction performance, thus ensuring effective noise cancellation without the need for inter-node communication. With the assistance of this mechanism, this approach significantly reduces both computational complexity and communication overhead. Numerical simulations employing real acoustic paths and compressor noise validate the effectiveness and robustness of the proposed system. The results demonstrate that our proposed method achieves satisfactory noise cancellation performance with minimal resource requirements.

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arXiv2025

Improving Stereo 3D Sound Event Localization and Detection: Perceptual Features, Stereo-specific Data Augmentation, and Distance Normalization

This technical report presents our submission to Task 3 of the DCASE 2025 Challenge: Stereo Sound Event Localization and Detection (SELD) in Regular Video Content. We address the audio-only task in this report and introduce several key contributions. First, we design perceptually-motivated input features that improve event detection, sound source localization, and distance estimation. Second, we adapt augmentation strategies specifically for the intricacies of stereo audio, including channel swapping and time-frequency masking. We also incorporate the recently proposed FilterAugment technique that has yet to be explored for SELD work. Lastly, we apply a distance normalization approach during training to stabilize regression targets. Experiments on the stereo STARSS23 dataset demonstrate consistent performance gains across all SELD metrics. Code to replicate our work is available in this repository: https://github.com/itsjunwei/NTU_SNTL_Task3

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arXiv2025

Mixed-gradients Distributed Filtered Reference Least Mean Square Algorithm -- A Robust Distributed Multichannel Active Noise Control Algorithm

Distributed multichannel active noise control (DMCANC), which utilizes multiple individual processors to achieve a global noise reduction performance comparable to conventional centralized multichannel active noise control (MCANC), has become increasingly attractive due to its high computational efficiency. However, the majority of current DMCANC algorithms disregard the impact of crosstalk across nodes and impose the assumption of an ideal network devoid of communication limitations, which is an unrealistic assumption. Therefore, this work presents a robust DMCANC algorithm that employs the compensating filter to mitigate the impact of crosstalk. The proposed solution enhances the DMCANC system's flexibility and security by utilizing local gradients instead of local control filters to convey enhanced information, resulting in a mixed-gradients distributed filtered reference least mean square (MGDFxLMS) algorithm. The performance investigation demonstrates that the proposed approach performs well with the centralized method. Furthermore, to address the issue of communication delay in the distributed network, a practical strategy that auto-shrinks the step size value in response to the delayed samples is implemented to improve the system's resilience. The numerical simulation results demonstrate the efficacy of the proposed auto-shrink step size MGDFxLMS (ASSS-MGDFxLMS) algorithm across various communication delays, highlighting its practical value.

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arXiv2025

Do neonates hear what we measure? Assessing neonatal ward soundscapes at the neonates ears

Acoustic guidelines for neonatal intensive care units (NICUs) aim to protect vulnerable neonates from noise-induced physiological harm. However, the lack of recognised international standards for measuring neonatal soundscapes has led to inconsistencies in instrumentation and microphone placement in existing literature, raising concerns about the relevance and effectiveness of these guidelines. This study addresses these gaps through long-term acoustic measurements in an operational NICU and a high-dependency ward. We investigate the influence of microphone positioning, bed placement, and ward layout on the assessment of NICU soundscapes. Beyond traditional A-weighted decibel metrics, this study evaluates C-weighted metrics for low-frequency noise, the occurrence of tonal sounds (e.g., alarms), and transient loud events known to disrupt neonates' sleep. Using linear mixed-effects models with aligned ranks transformation ANOVA (LME-ART-ANOVA), our results reveal significant differences in measured noise levels based on microphone placement, highlighting the importance of capturing sound as perceived directly at the neonate's ears. Additionally, bed position and ward layout significantly impact noise exposure, with a NICU bed position consistently exhibiting the highest sound levels across all (psycho)acoustic metrics. These findings support the adoption of binaural measurements along with the integration of additional (psycho)acoustic metrics, such as tonality and transient event occurrence rates, to reliably characterise the neonatal auditory experience.

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arXiv2024

Preventing output saturation in active noise control: An output-constrained Kalman filter approach

The Kalman filter (KF)-based active noise control (ANC) system demonstrates superior tracking and faster convergence compared to the least mean square (LMS) method, particularly in dynamic noise cancellation scenarios. However, in environments with extremely high noise levels, the power of the control signal can exceed the system's rated output power due to hardware limitations, leading to output saturation and subsequent non-linearity. To mitigate this issue, a modified KF with an output constraint is proposed. In this approach, the disturbance treated as an measurement is re-scaled by a constraint factor, which is determined by the system's rated power, the secondary path gain, and the disturbance power. As a result, the output power of the system, i.e. the control signal, is indirectly constrained within the maximum output of the system, ensuring stability. Simulation results indicate that the proposed algorithm not only achieves rapid suppression of dynamic noise but also effectively prevents non-linearity due to output saturation, highlighting its practical significance.

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arXiv2024

FRCRN: Boosting Feature Representation using Frequency Recurrence for Monaural Speech Enhancement

Convolutional recurrent networks (CRN) integrating a convolutional encoder-decoder (CED) structure and a recurrent structure have achieved promising performance for monaural speech enhancement. However, feature representation across frequency context is highly constrained due to limited receptive fields in the convolutions of CED. In this paper, we propose a convolutional recurrent encoder-decoder (CRED) structure to boost feature representation along the frequency axis. The CRED applies frequency recurrence on 3D convolutional feature maps along the frequency axis following each convolution, therefore, it is capable of catching long-range frequency correlations and enhancing feature representations of speech inputs. The proposed frequency recurrence is realized efficiently using a feedforward sequential memory network (FSMN). Besides the CRED, we insert two stacked FSMN layers between the encoder and the decoder to model further temporal dynamics. We name the proposed framework as Frequency Recurrent CRN (FRCRN). We design FRCRN to predict complex Ideal Ratio Mask (cIRM) in complex-valued domain and optimize FRCRN using both time-frequency-domain and time-domain losses. Our proposed approach achieved state-of-the-art performance on wideband benchmark datasets and achieved 2nd place for the real-time fullband track in terms of Mean Opinion Score (MOS) and Word Accuracy (WAcc) in the ICASSP 2022 Deep Noise Suppression (DNS) challenge (https://github.com/modelscope/ClearerVoice-Studio).

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arXiv2024

AudioSetCaps: An Enriched Audio-Caption Dataset using Automated Generation Pipeline with Large Audio and Language Models

With the emergence of audio-language models, constructing large-scale paired audio-language datasets has become essential yet challenging for model development, primarily due to the time-intensive and labour-heavy demands involved. While large language models (LLMs) have improved the efficiency of synthetic audio caption generation, current approaches struggle to effectively extract and incorporate detailed audio information. In this paper, we propose an automated pipeline that integrates audio-language models for fine-grained content extraction, LLMs for synthetic caption generation, and a contrastive language-audio pretraining (CLAP) model-based refinement process to improve the quality of captions. Specifically, we employ prompt chaining techniques in the content extraction stage to obtain accurate and fine-grained audio information, while we use the refinement process to mitigate potential hallucinations in the generated captions. Leveraging the AudioSet dataset and the proposed approach, we create AudioSetCaps, a dataset comprising 1.9 million audio-caption pairs, the largest audio-caption dataset at the time of writing. The models trained with AudioSetCaps achieve state-of-the-art performance on audio-text retrieval with R@1 scores of 46.3% for text-to-audio and 59.7% for audio-to-text retrieval and automated audio captioning with the CIDEr score of 84.8. As our approach has shown promising results with AudioSetCaps, we create another dataset containing 4.1 million synthetic audio-language pairs based on the Youtube-8M and VGGSound datasets. To facilitate research in audio-language learning, we have made our pipeline, datasets with 6 million audio-language pairs, and pre-trained models publicly available at https://github.com/JishengBai/AudioSetCaps.

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arXiv2024

Automating Urban Soundscape Enhancements with AI: In-situ Assessment of Quality and Restorativeness in Traffic-Exposed Residential Areas

Formalized in ISO 12913, the "soundscape" approach is a paradigmatic shift towards perception-based urban sound management, aiming to alleviate the substantial socioeconomic costs of noise pollution to advance the United Nations Sustainable Development Goals. Focusing on traffic-exposed outdoor residential sites, we implemented an automatic masker selection system (AMSS) utilizing natural sounds to mask (or augment) traffic soundscapes. We employed a pre-trained AI model to automatically select the optimal masker and adjust its playback level, adapting to changes over time in the ambient environment to maximize "Pleasantness", a perceptual dimension of soundscape quality in ISO 12913. Our validation study involving ($N=68$) residents revealed a significant 14.6 % enhancement in "Pleasantness" after intervention, correlating with increased restorativeness and positive affect. Perceptual enhancements at the traffic-exposed site matched those at a quieter control site with 6 dB(A) lower $L_\text{A,eq}$ and road traffic noise dominance, affirming the efficacy of AMSS as a soundscape intervention, while streamlining the labour-intensive assessment of "Pleasantness" with probabilistic AI prediction.

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arXiv2024

Real-Time Sound Event Localization and Detection: Deployment Challenges on Edge Devices

Sound event localization and detection (SELD) is critical for various real-world applications, including smart monitoring and Internet of Things (IoT) systems. Although deep neural networks (DNNs) represent the state-of-the-art approach for SELD, their significant computational complexity and model sizes present challenges for deployment on resource-constrained edge devices, especially under real-time conditions. Despite the growing need for real-time SELD, research in this area remains limited. In this paper, we investigate the unique challenges of deploying SELD systems for real-world, real-time applications by performing extensive experiments on a commercially available Raspberry Pi 3 edge device. Our findings reveal two critical, often overlooked considerations: the high computational cost of feature extraction and the performance degradation associated with low-latency, real-time inference. This paper provides valuable insights and considerations for future work toward developing more efficient and robust real-time SELD systems

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arXiv2024

Data Efficient Acoustic Scene Classification using Teacher-Informed Confusing Class Instruction

In this technical report, we describe the SNTL-NTU team's submission for Task 1 Data-Efficient Low-Complexity Acoustic Scene Classification of the detection and classification of acoustic scenes and events (DCASE) 2024 challenge. Three systems are introduced to tackle training splits of different sizes. For small training splits, we explored reducing the complexity of the provided baseline model by reducing the number of base channels. We introduce data augmentation in the form of mixup to increase the diversity of training samples. For the larger training splits, we use FocusNet to provide confusing class information to an ensemble of multiple Patchout faSt Spectrogram Transformer (PaSST) models and baseline models trained on the original sampling rate of 44.1 kHz. We use Knowledge Distillation to distill the ensemble model to the baseline student model. Training the systems on the TAU Urban Acoustic Scene 2022 Mobile development dataset yielded the highest average testing accuracy of (62.21, 59.82, 56.81, 53.03, 47.97)% on split (100, 50, 25, 10, 5)% respectively over the three systems.

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arXiv2024

Transferable Selective Virtual Sensing Active Noise Control Technique Based on Metric Learning

Virtual sensing (VS) technology enables active noise control (ANC) systems to attenuate noise at virtual locations distant from the physical error microphones. Appropriate auxiliary filters (AF) can significantly enhance the effectiveness of VS approaches. The selection of appropriate AF for various types of noise can be automatically achieved using convolutional neural networks (CNNs). However, training the CNN model for different ANC systems is often labour-intensive and time-consuming. To tackle this problem, we propose a novel method, Transferable Selective VS, by integrating metric-learning technology into CNN-based VS approaches. The Transferable Selective VS method allows a pre-trained CNN to be applied directly to new ANC systems without requiring retraining, and it can handle unseen noise types. Numerical simulations demonstrate the effectiveness of the proposed method in attenuating sudden-varying broadband noises and real-world noises.

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arXiv2024

A Real-Time Platform for Portable and Scalable Active Noise Mitigation for Construction Machinery

This paper introduces a novel portable and scalable Active Noise Mitigation (PSANM) system designed to reduce low-frequency noise from construction machinery. The PSANM system consists of portable units with autonomous capabilities, optimized for stable performance within a specific power range. An adaptive control algorithm with a variable penalty factor prevents the adaptive filter from over-driving the anti-noise actuators, avoiding non-linear operation and instability. This feature ensures the PSANM system can autonomously control noise at its source, allowing for continuous operation without human intervention. Additionally, the system includes a web server for remote management and is equipped with weather-resistant sensors and actuators, enhancing its usability in outdoor conditions. Laboratory and in-situ experiments demonstrate the PSANM system's effectiveness in reducing construction-related low-frequency noise on a global scale. To further expand the noise reduction zone, additional PSANM units can be strategically positioned in front of noise sources, enhancing the system's scalability.The PSANM system also provides a valuable prototyping platform for developing adaptive algorithms prior to deployment. Unlike many studies that rely solely on simulation results under ideal conditions, this paper offers a holistic evaluation of the effectiveness of applying active noise control techniques directly at the noise source, demonstrating realistic and perceptible noise reduction. This work supports sustainable urban development by offering innovative noise management solutions for the construction industry, contributing to a quieter and more livable urban environment.

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arXiv2024

Extracting Urban Sound Information for Residential Areas in Smart Cities Using an End-to-End IoT System

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.

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arXiv2024

Squeeze-and-Excite ResNet-Conformers for Sound Event Localization, Detection, and Distance Estimation for DCASE 2024 Challenge

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.

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arXiv2024

Autonomous Soundscape Augmentation with Multimodal Fusion of Visual and Participant-linked Inputs

Autonomous soundscape augmentation systems typically use trained models to pick optimal maskers to effect a desired perceptual change. While acoustic information is paramount to such systems, contextual information, including participant demographics and the visual environment, also influences acoustic perception. Hence, we propose modular modifications to an existing attention-based deep neural network, to allow early, mid-level, and late feature fusion of participant-linked, visual, and acoustic features. Ablation studies on module configurations and corresponding fusion methods using the ARAUS dataset show that contextual features improve the model performance in a statistically significant manner on the normalized ISO Pleasantness, to a mean squared error of $0.1194\pm0.0012$ for the best-performing all-modality model, against $0.1217\pm0.0009$ for the audio-only model. Soundscape augmentation systems can thereby leverage multimodal inputs for improved performance. We also investigate the impact of individual participant-linked factors using trained models to illustrate improvements in model explainability.

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