Environmental Sound Classification Using Local Binary Pattern and Audio Features Collaboration

Abstract

This paper presents a new approach to classify environmental sounds using a texture feature local binary pattern (LBP) and audio features collaboration. To our knowledge, this is the first time that the LBP (or its variants), which has a proven track record in the field of image recognition and classification, has been generalized for 1D and combined with audio features for an environmental sound classification task. To this end, we have generalized and defined LBP-1D and local phase quantization (LPQ)-1D on the 1-dimensional (1D) audio signal and have applied the original LBP, the variance LBP (VARLBP) and the extended LBP (ELBP) thus generated to the spectrogram of the audio signal in order to model the sound texture. We have also extensively compared these new LBP-based features to the classical audio descriptors commonly used in environmental sound classification, such as MFCC, GFCC, CQT, chromagram, STE and ZCR. We have evaluated our algorithm on ESC-10 and ESC-50 datasets using classical machine learning algorithms, such as support vector machines (SVM), random forest and k-nearest neighbor (kNN). The results showed that the LBP features outperform the classical audio features. We mix the LBP features with the audio descriptors, and our best mixed model achieves state-of-the-art results for environmental sound classification: 88.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on ESC-10 and 64.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on ESC-50. Those results outperform the results of methods that used handcrafted features with classical machine learning algorithms and are similar to some convolutional neural network-based methods. Although our method is not the cutting edge of the state-of-the-art methods, it is faster than any convolutional neural network methods and represents a better choice when there is data scarcity or minimal computing power.