Search arXiv⌕ Search

arXiv · 2409.10534

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

Abstract

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Woon-Seng Gan, Santi Peksi, Chung Kwan Lai, Yen Theng Lee, Dongyuan Shi, Bhan Lam. 2024-08-31. A Real-Time Platform for Portable and Scalable Active Noise Mitigation for Construction Machinery. https://arxiv.org/abs/2409.10534

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deep Filter Estimation from Inter-Frame Correlations for Monaural Speech Dereverberation

Speech dereverberation with a distant microphone is challenging because reverberation is correlated with the target speech, and models trained on simulated data often generalize poorly to real recordings. We propose IF-CorrNet, a correlation-to-filter architecture for monaural dereverberation. Instead of feeding raw complex STFT coefficients to the network, IF-CorrNet computes inter-frame correlations among neighboring frames at each time-frequency bin and estimates multi-frame deep filters from these features with a dual-path Transformer backbone. This design makes inter-frame dependencies explicit at the network input while retaining a multi-frame filtering output, a pairing motivated by the normal equation of linear multi-frame filtering. On the REVERB Challenge corpus, IF-CorrNet achieves the best CD, LLR, SNRfw, and PESQ among the compared dereverberation baselines on SimData, and the highest SRMR among the compared systems on RealData. The ablation shows higher RealData SRMR with correlation inputs for both filtering and masking, with filtering adding a further gain.

eess.AS↗

Recovering the Zipfian Distribution in Unsupervised Term Discovery

Unsupervised term discovery involves segmenting unlabelled speech into word- or syllable-like units and clustering these into a lexicon of candidate types. True lexicons follow a Zipfian distribution, yet the dominant centre-based clustering approach -- K-means -- produces a more uniform distribution due to an inductive bias toward spherical clusters. In this paper we revisit graph-based clustering as a bottom-up alternative, where segment embeddings are connected by pairwise similarity and partitioned using the Leiden algorithm. We show that graph clustering substantially outperforms centre-based approaches (K-means, GMM, BIRCH) in both word- and syllable-level lexicon discovery across three languages, producing more Zipf-like distributions. Another bottom-up approach, agglomerative clustering with average linkage, also performs well, although it is computationally less efficient and allows for less control over the resulting distribution. Our work calls into question the dominance of centre-based clustering for term discovery, and promotes graph clustering as an attractive alternative.

eess.AS↗

CHILDES-Aligned: A Curated Children's Speech Dataset via Multi-Model Timestamp Ensembling

CHILDES is a large-scale child speech corpus containing long-form recordings of naturalistic child-adult interactions, making it a valuable resource for studying child speech and language development. However, utterance-level timestamps provided in this corpus are often noisy, incomplete, or misaligned with the audio. As a result, utterances cannot always be reliably localized within long recordings, which limits the direct use of these data for training and evaluating speech models. In this work, we propose BEACON (Boundary Estimation via Alignment CONsensus), an ensemble timestamp-curation framework that refines utterance-level timestamps by aggregating knowledge from multiple off-the-shelf ASR models. Specifically, each model's word-level timestamp predictions are first aligned to provided human transcripts, and the final utterance time boundaries are determined by a consensus voting strategy. The framework is corpus-agnostic and applies to any long-form recording paired with a trusted transcript whose timestamps are unreliable or missing, offering a general recipe for timestamp curation. Leveraging this pipeline, we curate and release a 413-hour general-purpose child-speech dataset with corrected utterance-level timestamps, together with a 283-hour quality-controlled subset for ASR training. Fine-tuning on this subset yields up to an average 19.5% relative WER reduction on four out-of-domain child-speech benchmarks.

eess.AS↗