Multi-Label Sound Event Retrieval Using A Deep Learning-Based Siamese Structure With A Pairwise Presence Matrix

Fan, Jianyu; Nichols, Eric; Tompkins, Daniel; Méndez, Ana Elisa Méndez; Elizalde, Benjamin; Pasquier, Philippe

doi:10.1109/icassp40776.2020.9053972

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Using this data-set, methods for both detection [35] and classification [36] of acoustic scenes and events have been carried out. Recently, a deep learning structure has been developed with this data-set for sound event retrieval [37] of urban sound events, such as car horns and human speech, on multi-label audio recordings.…”

Section: Machine Learning For Analysis Of Environmental Acousticsmentioning

confidence: 99%

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

Pita

Rodríguez

Navarro

2021

IJERPH

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As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.

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Section: Machine Learning For Analysis Of Environmental Acousticsmentioning

confidence: 99%

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

Pita

Rodríguez

Navarro

2021

IJERPH

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Memetic multilabel feature selection using pruned refinement process

Seo,

Park,

Lee

et al. 2024

J Big Data

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Machine Learning Prediction of the Long-Term Environmental Acoustic Pattern of a City Location Using Short-Term Sound Pressure Level Measurements

Navarro

Pita

2023

Applied Sciences

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To manage noise pollution, cities use monitoring systems over wireless acoustic sensor networks. These networks are mainly composed of fixed-location sound pressure level sensors deployed in outdoor sites of the city for long-term monitoring. However, due to high economic and human resource costs, it is not feasible to deploy fixed metering stations on every street in a city. Therefore, these continuous measurements are usually complemented with short-term measurements at different selected locations, which are carried out by acoustic sensors mounted on vehicles or at street level. In this research, the application of artificial neural networks is proposed for estimation of the long-term environmental acoustic pattern of a location based on the information collected during a short time period. An evaluation has been carried out through a comparison of eight artificial neural network architectures using real data from the acoustic sensor network of Barcelona, Spain, showing higher accuracy in prediction when the complexity of the model increases. Moreover, time slots with better performance can be detected, helping city managers to deploy temporal stations optimally.

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Multi-Label Sound Event Retrieval Using A Deep Learning-Based Siamese Structure With A Pairwise Presence Matrix

Cited by 3 publications

References 21 publications

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

Cluster Analysis of Urban Acoustic Environments on Barcelona Sensor Network Data

Memetic multilabel feature selection using pruned refinement process

Machine Learning Prediction of the Long-Term Environmental Acoustic Pattern of a City Location Using Short-Term Sound Pressure Level Measurements

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