Soft computing based feature selection for environmental sound classification (original) (raw)
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Hybrid Computerized Method for Environmental Sound Classification
IEEE Access, 2020
Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) creates a need to be urgently satisfied. As a response to this limitation, in this paper, a hybrid model for automatic and accurate classification of environmental sounds is proposed. Optimum allocation sampling (OAS) is used to elicit the informative samples from each class. The representative samples obtained by OAS are turned into the spectrogram containing their time-frequency-amplitude representation by using a short-time Fourier transform (STFT). The spectrogram is then given as an input to pre-trained AlexNet and Visual Geometry Group (VGG)-16 networks. Multiple deep features are extracted using the pre-trained networks and classified by using multiple classification techniques namely decision tree (fine, medium, coarse kernel), k-nearest neighbor (fine, medium, cosine, cubic, coarse and weighted kernel), support vector machine, linear discriminant analysis, bagged tree and softmax classifiers. The ESC-10, a ten-class environmental sound dataset, is used for the evaluation of the methodology. An accuracy of 90.1%, 95.8%, 94.7%, 87.9%, 95.6%, and 92.4% is obtained with a decision tree, k-neared neighbor, support vector machine, linear discriminant analysis, bagged tree and softmax classifier respectively. The proposed method proved to be robust, effective, and promising in comparison with other existing state-of-the-art techniques, using the same dataset. INDEX TERMS Environmental sound classification, optimal allocation sampling, spectrogram, convolutional neural network, classification techniques.
On feature selection in environmental sound recognition
Given a broad set of content-based audio features, we employ principal component analysis for the composition of an optimal feature set for environmental sounds. We select features based on quantitative data analysis (factor analysis) and conduct retrieval experiments to evaluate the quality of the feature combinations. Retrieval results show that statistical data analysis gives useful hints for feature selection. The experiments show the importance of feature selection in environmental sound recognition.
Robust features for environmental sound classification
2013 IEEE International Conference on Electronics, Computing and Communication Technologies, 2013
ABSTRACT In this paper we describe algorithms to classify environmental sounds with the aim of providing contextual information to devices such as hearing aids for optimum performance. We use signal sub-band energy to construct signal-dependent dictionary and matching pursuit algorithms to obtain a sparse representation of a signal. The coefficients of the sparse vector are used as weights to compute weighted features. These features, along with mel frequency cepstral coefficients (MFCC), are used as feature vectors for classification. Experimental results show that the proposed method gives an accuracy as high as 95.6 %, while classifying 14 categories of environmental sound using a gaussian mixture model (GMM).
Feature Selection for Place Classification through Environmental Sounds
Procedia Computer Science, 2014
In this work, an environmental audio classification scheme is proposed using a Chi squared filter as a feature selection strategy. Using feature selection (FS), the original 62 features characteristic vector can be optimized, and it can be used for environmental sound classification. These features are obtained using statistical analysis and frequency domain analysis. As a result, we obtain a reduced feature vector composed of 15 features: 11 statistical and 4 of the frequency domain. Using this reduced vector, a 10 class classification was done, using Support Vector machines (SVM) as classification method, the accuracy is higher than 90%.
International Journal of Software Science and Computational Intelligence, 2012
Feature selection or attribute reduction is performed mainly to avoid the ‘curse of dimensionality’ in the large database problem including musical instrument sound classification. This problem deals with the irrelevant and redundant features. Rough set theory and soft set theory proposed by Pawlak and Molodtsov, respectively, are mathematical tools for dealing with the uncertain and imprecision data. Rough and soft set-based dimensionality reduction can be considered as machine learning approaches for feature selection. In this paper, the authors applied these approaches for data cleansing and feature selection technique of Traditional Malay musical instrument sound classification. The data cleansing technique is developed based on matrices computation of multi-soft sets while feature selection using maximum attributes dependency based on rough set theory. The modeling process comprises eight phases: data acquisition, sound editing, data representation, feature extraction, data dis...
PERFORMANCE ACCURACY OF CLASSIFICATION ON ENVIRONMENTAL SOUND CLASSIFICATION (ESC_50) DATASET
IJCIRAS, 2020
The classification of audio dataset is intended to distinguish between the different source of audio such as indoor, outdoor and environmental sounds. The environmental sound classification (ESC-50) dataset is composed with a labeled set of 2000 environmental recordings. The spectral centroid method is applied to extract audio features from ESC-50 dataset with waveform audio file (WAV) format. The decision tree is easy to implement and fast for fitting and prediction therefore this proposed system is utilized the coarse tree and medium tree as a classifier. Then fivefold cross-validation is also applied to evaluate the performance of classifier. The proposed system is implemented by using Matlab programming. The classification accuracy of coarse tree is 63.8% whereas the medium tree is 58.6% on ESC-50 dataset.
Towards an optimal feature set for environmental sound recognition
Feature selection for audio retrieval is a non-trivial task. In this paper we aim at identifying an optimal feature combination for environmental sound recognition. The feature combination is constructed from a broad set of features. Additionally to state-of-the-art features, we evaluate the quality of audio features we previously introduced for another domain. We examine the properties of features by quantitative data analysis (factor analysis) and identify candidates for feature combinations. We verify the quality of the combination by retrieval experiments. The optimal solution yields Recall and Precision values of 87% and 88%, respectively.
Studies in fuzziness and soft computing, 1999
Cataloging-in-Publication Data applied for Die Deutsche Bibliothek-CIP-Einheitsaufnahme Kostek, Bozena: Soft computing in acoustics: applications of neural networks, fuzzy logic and rough sets to musical acoustics; with 84 tables I Bozena Kostek.