Sound Classification Based on Multihead Attention and Support Vector Machine

Abstract

Sound classification is a broad area of research that has gained much attention in recent years. The sound classification systems based on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have undergone significant enhancements in the recognition capability of models. However, their computational complexity and inadequate exploration of global dependencies for long sequences restrict improvements in their classification results. In this paper, we show that there are still opportunities to improve the performance of sound classification by substituting the recurrent architecture with the parallel processing structure in the feature extraction. In light of the small-scale and high-dimension sound datasets, we propose the use of the multihead attention and support vector machine (SVM) for sound taxonomy. The multihead attention is taken as the feature extractor to obtain salient features, and SVM is taken as the classifier to recognize all categories. Extensive experiments are conducted across three acoustically characterized public datasets, UrbanSound8K, GTZAN, and IEMOCAP, by using two commonly used audio spectrograms as inputs, respectively, and we fully evaluate the impact of parameters and feature types on classification accuracy. Our results suggest that the proposed model can reach comparable performance with existing methods and reveal its strong generalization ability of sound taxonomy.

Classification of Indian Classical Music (Hindustani Music) Genres through MFCCs Features using RNN-LSTM Model

“…The accuracy of the model can be defined by the formula given below: 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁 ( 6 ) 3…”

“…Previously, researchers used mathematical techniques like standard statistical pattern recognition (SPR), Gaussian classifier (GS), and Gaussian Mixture Model (GMM) to classify music genres. In the age of AI, researchers have been using various techniques of Machine learning like Multi-Class Support Vector Machine (SVM) [6], K-Nearest Neighbors (KNN) [7], Linear Kernel SVM, Polynomial Kernel SVM, Decision Tree, Random Forest, Ada Boost, Naïve Bayes, Linear Discriminant Analysis (LDA) classifier, Logistic Regression, and Sigmoid Kernel SVM [6], [8]- [10]. In modern days, deep learning is not only solving the problem of computer vision but also dealing with sequencing and time series problems [11][12].…”

Live Event Detection for People’s Safety Using NLP and Deep Learning

“…Returning to the classification of sound/noise, which can be classically used as the base for the detection danger from the noise around an individual/population, [15] demonstrates that sound categorization performance can still be improved by swapping out the recurrent architecture for a parallel processing structure during feature extraction. The research processes the huge data and uses it to develop the model using Deep Learning Algorithms, namely CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory).…”

“…In this research, the audio analysis technique adopted is the Fourier Transform and Mel-Spectrogram (similar to [31]), and the audio was sampled at 44.1kHz (just like in [32]) for further processing. Post-cleaning, the sound data is subjected to three different deep learning models (1D-CNN, 2D-CNN, and LSTM) for the classification of sound from a person's surroundings (the likes of which have been used in various pieces of research cited above, for example: [15]), and to detect a threat from it. If the threat is detected, then an automatic alert message is sent to the registered help or the emergency services.…”

Research on Audio Recognition Based on the Deep Neural Network in Music Teaching

“…In the aspect of audio recognition, Yang and Zhao [ 17 ] proposed an acoustic scene classification method based on the support vector machine (SVM), which enhanced the sound texture to improve the classification accuracy. Greco et al [ 18 ] proposed a voice recognition system based on the heuristic deep learning method.…”