Monitoring, profiling and classification of urban environmental noise using sound characteristics and the KNN algorithm

Tsalera, Eleni; Papadakis, Andreas; Σαμαράκου, Μαρία

doi:10.1016/j.egyr.2020.08.045

Cited by 29 publications

(25 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In classic ML techniques, sound features are extracted and used in the algorithms. Features are based on psychoacoustic properties of sounds such as loudness, pitch and timbre, while cepstral features are also widely used, including Mel-Frequency Cepstral Coefficients (MFCC) and their derivatives [16,17]. Feature evaluation methodologies are employed such as Relief-F [18] and Principal Component Analysis (PCA) based [19].…”

Section: Sound Classificationmentioning

confidence: 99%

Comparison of Pre-Trained CNNs for Audio Classification Using Transfer Learning

Tsalera

Papadakis

Σαμαράκου

2021

JSAN

Self Cite

View full text Add to dashboard Cite

The paper investigates retraining options and the performance of pre-trained Convolutional Neural Networks (CNNs) for sound classification. CNNs were initially designed for image classification and recognition, and, at a second phase, they extended towards sound classification. Transfer learning is a promising paradigm, retraining already trained networks upon different datasets. We selected three ‘Image’- and two ‘Sound’-trained CNNs, namely, GoogLeNet, SqueezeNet, ShuffleNet, VGGish, and YAMNet, and applied transfer learning. We explored the influence of key retraining parameters, including the optimizer, the mini-batch size, the learning rate, and the number of epochs, on the classification accuracy and the processing time needed in terms of sound preprocessing for the preparation of the scalograms and spectrograms as well as CNN training. The UrbanSound8K, ESC-10, and Air Compressor open sound datasets were employed. Using a two-fold criterion based on classification accuracy and time needed, we selected the ‘champion’ transfer-learning parameter combinations, discussed the consistency of the classification results, and explored possible benefits from fusing the classification estimations. The Sound CNNs achieved better classification accuracy, reaching an average of 96.4% for UrbanSound8K, 91.25% for ESC-10, and 100% for the Air Compressor dataset.

show abstract

Section: Sound Classificationmentioning

confidence: 99%

Comparison of Pre-Trained CNNs for Audio Classification Using Transfer Learning

Tsalera

Papadakis

Σαμαράκου

2021

JSAN

Self Cite

View full text Add to dashboard Cite

show abstract

“…The other is the number of adjacent samples selected, denoted as k. The category of each sample in the database has the greatest correlation with the k nearest samples in the feature space. [31][32][33][34] In this study, the Euclidean distance was selected as the distance metric, and the trial and error method was employed to search the optimal value of parameter k within the range of 1-30. As the characteristics of different steel grades are mainly reflected in their compositions and processes, the input parameters to the KNN model were selected from the parameter information shown in Table 1, and the following three different schemes were designed to develop classification models.…”

Section: Prediction Frameworkmentioning

confidence: 99%

Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction

Huang

et al. 2022

steel research int.

View full text Add to dashboard Cite

show abstract

“…In order to verify the effectiveness and compatibility of the proposed feature extraction method, two widely used pattern recognisers, BT-SVM and KNN, were used in this article for classification [34][35][36][37].…”

Section: Effectiveness Comparison Of Classification: Bt-svm and Weigh...mentioning

confidence: 99%

Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF

Chen

Shao

Pan

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

Low-temperature composite insulation is commonly applied in high-temperature superconducting apparatus while partial discharge (PD) is found to be an important indicator to reveal insulation statues. In order to extract feature parameters of PD signals more effectively, a method combined variational mode decomposition with multi-scale entropy and image feature is proposed. Based on the simulated test platform, original and noisy signals of three typical PD defects were obtained and decomposed. Accordingly, relative moments and grayscale co-occurrence matrix were employed for feature extraction by Kmodal component diagram. Afterwards, new PD feature vectors were obtained by dimension reduction. Finally, effectiveness of different feature extraction methods was evaluated by pattern recognition based on support vector machine and K-nearest neighbour. Result shows that the proposed feature extraction method has a higher recognition rate by comparison and is robust in processing noisy signals.

show abstract

Monitoring, profiling and classification of urban environmental noise using sound characteristics and the KNN algorithm

Cited by 29 publications

References 10 publications

Comparison of Pre-Trained CNNs for Audio Classification Using Transfer Learning

Comparison of Pre-Trained CNNs for Audio Classification Using Transfer Learning

Physical Metallurgy Guided Industrial Big Data Analysis System with Data Classification and Property Prediction

Feature extraction of partial discharge in low‐temperature composite insulation based on VMD‐MSE‐IF

Contact Info

Product

Resources

About