Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Nogueira, Ana Filipa Rodrigues; Oliveira, Hugo S.; Machado, J.J.M.; Tavares, João Manuel R. S.

doi:10.3390/s22228608

Cited by 19 publications

(6 citation statements)

References 70 publications

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“…Another key application of ESC is urban sound classification, which delves into the acoustic ecology within urban landscapes, notably cities. With advanced approaches such as DL-based audio classifiers on edge devices, urban sound classification holds immense potential for designing, managing, and monitoring sustainable urban environments that promote human well-being, and security, and can minimize noise pollution alongside ecological diversity [ 16 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge

Ranmal,

Ranasinghe,

Paranayapa

et al. 2024

Sensors

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The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop deep convolutional neural network architectures specifically tailored for handling raw audio inputs in environmental sound classification applications under limited computational resources. The ESC-NAS process consists of a novel cell-based neural architecture search space built with 2D convolution, batch normalization, and max pooling layers, and capable of extracting features from raw audio. A black-box Bayesian optimization search strategy explores the search space and the resulting model architectures are evaluated through hardware simulation. The models obtained from the ESC-NAS process achieved the optimal trade-off between model performance and resource consumption compared to the existing literature. The ESC-NAS models achieved accuracies of 85.78%, 81.25%, 96.25%, and 81.0% for the FSC22, UrbanSound8K, ESC-10, and ESC-50 datasets, respectively, with optimal model sizes and parameter counts for edge deployment.

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Section: Introductionmentioning

confidence: 99%

ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge

Ranmal,

Ranasinghe,

Paranayapa

et al. 2024

Sensors

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“…Sound is a complex, feature-rich signal, and sound classification is receiving strong interest in a growing number of application areas, from speech recognition [ 1 , 2 ], music analysis and recommendation [ 3 , 4 ], environmental sound monitoring [ 5 , 6 ], and anomaly detection and security [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Environmental sound monitoring [ 5 , 6 ]: Sound classification can be used to monitor and classify environmental sounds. This is useful in applications such as wildlife monitoring, noise pollution assessment, acoustic event detection, and surveillance systems.…”

Section: Introductionmentioning

confidence: 99%

A CNN Sound Classification Mechanism Using Data Augmentation

Chu,

Zhang,

Chiang

2023

Sensors

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Sound classification has been widely used in many fields. Unlike traditional signal-processing methods, using deep learning technology for sound classification is one of the most feasible and effective methods. However, limited by the quality of the training dataset, such as cost and resource constraints, data imbalance, and data annotation issues, the classification performance is affected. Therefore, we propose a sound classification mechanism based on convolutional neural networks and use the sound feature extraction method of Mel-Frequency Cepstral Coefficients (MFCCs) to convert sound signals into spectrograms. Spectrograms are suitable as input for CNN models. To provide the function of data augmentation, we can increase the number of spectrograms by setting the number of triangular bandpass filters. The experimental results show that there are 50 semantic categories in the ESC-50 dataset, the types are complex, and the amount of data is insufficient, resulting in a classification accuracy of only 63%. When using the proposed data augmentation method (K = 5), the accuracy is effectively increased to 97%. Furthermore, in the UrbanSound8K dataset, the amount of data is sufficient, so the classification accuracy can reach 90%, and the classification accuracy can be slightly increased to 92% via data augmentation. However, when only 50% of the training dataset is used, along with data augmentation, the establishment of the training model can be accelerated, and the classification accuracy can reach 91%.

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“…Because of this, the classification of sounds has become a very popular topic. Fields of application include, for example: multimedia retrieval [1,2], technology medical problems s [3,4], speech recognition [5], speaker recognition [6,7], urban sound classification [8,9], environmental sound classification [10,11], speech emotion recognition [12], animal sound classification [13,14], detection of mechanical failure [15], and many others. In recognition tasks, the basic issue is what to recognize, in other words, what the inputs of the system are.…”

Section: Introductionmentioning

confidence: 99%

Classification of Engine Type of Vehicle Based on Audio Signal as a Source of Identification

Materlak

Majda-Zdancewicz

2023

Electronics

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In this work, a combination of signal processing and machine learning techniques is applied for petrol and diesel engine identification based on engine sound. The research utilized real recordings acquired in car dealerships within Poland. The sound database recorded by the authors contains 80 various audio signals, equally divided. The study was conducted using feature engineering techniques based on frequency analysis for the generation of sound signal features. The discriminatory ability of feature vectors was evaluated using different machine learning techniques. In order to test the robustness of the proposed solution, the authors executed a number of system experimental tests, including different work conditions for the proposed system. The results show that the proposed approach produces a good accuracy at a level of 91.7%. The proposed system can support intelligent transportation systems through employing a sound signal as a medium carrying information on the type of car moving along a road. Such solutions can be implemented in the so-called ‘clean transport zones’, where only petrol-powered vehicles can freely move. Another potential application is to prevent misfuelling diesel to a petrol engine or petrol to a diesel engine. This kind of system can be implemented in petrol stations to recognize the vehicle based on the sound of the engine.

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Sound Classification and Processing of Urban Environments: A Systematic Literature Review

Cited by 19 publications

References 70 publications

ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge

ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge

A CNN Sound Classification Mechanism Using Data Augmentation

Classification of Engine Type of Vehicle Based on Audio Signal as a Source of Identification

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