Audio classification using grasshopper‐ride optimization algorithm‐based support vector machine

Abstract

The accurate and robust detection of the audio has been widely grown as the speech technology in the area of audio forensics, speech recognition, and so on. However, in real time, it is a challenge to deal with the massive data arriving from distributed sources. Thus, the study introduces a method that effectively deals with the data from the distributed sources using the map-reduce framework (MRF). The map and reduce function in MRF aim at feature extraction and audio classification. The robust classification using the proposed grasshopper-ride optimization algorithm-based support vector machine (G-ROA-based SVM) uses the features, such as multiple kernel Mel frequency cepstral coefficients, spectral flux, spectral kurtosis, and delta-amplitude modulation spectrogram. The proposed G-ROA is the integration of ROA and grasshopper optimization algorithm in tuning the optimal weights of SVM and also, the kernel function in SVM is modified using the Gaussian radial basis function, Gaussian kernel, and polynomial kernels. The experimentation of the proposed method is done using two datasets, namely TUT sound event 2017 dataset and ESC dataset. TUT sound event 2017 dataset consists of eight audio recordings from a single acoustic scene. ESC dataset consists of three parts and 252,400 recordings. The analysis reveals that the proposed audio classification acquired the maximal accuracy of 0.96, minimal false alarm rate, and false rejection rate of 0.022 and 0.0119, respectively. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Research on automatic detection of gradual fault of high voltage electric energy metering transformer based on fuzzy rough set and whale optimization algorithm

“…As an effective and widely used mainstream classification algorithm [19] in machine learning, Support Vector Machine (SVM) can not only classify linearly separable data, but also have excellent classification effect on nonlinear separable and nonlinear inseparable data. Support vector machine can balance the relationship between learning ability and the complexity of fault classification model according to the known fault sample data, and it does not need a lot of fault sample data, so it is very suitable for fault classification of complex chemical systems [20]. However, SVM parameters are an important factor affecting its classification performance.…”

Acoustic scene classification based on three-dimensional multi-channel feature-correlated deep learning networks

“…Features in the time-domain and frequency-domain can be generated based on signal waveforms, e.g., Short-Time Fourier Transforms (STFT) 6 , Mel-Scale (Mel) Spectrogram 7 , Mel-Scale Frequency Cepstral Coefficients (MFCC) 8 , Constant-Q Transform (CQT) 9 and various one-dimensional (1D) spectral properties. Traditionally, statistical models are used in classification tasks, such as Hidden Markov Model (HMMs) 10 , Gaussian Mixture Model (GMM) 11 , 12 , and Support Vector Machines (SVM) 13 , 14 .…”