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Teaching Machines on Snoring : A Benchmark on Computer Audition for Snore Sound Excitation Localisation

Qian K., Janott C., Zhang Z., Deng J., Baird A., Heiser C., Hohenhorst W., Herzog M., Hemmert W., Schuller B.

Archives of Acoustics

2018

Vol. 43, No. 3

465--475

This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband Energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.

Classification of Music Genres Based on Music Separation into Harmonic and Drum Components

Rosner A., Schuller B., Kostek B.

Archives of Acoustics

2014

Vol. 39, No. 4

629--638

This article presents a study on music genre classification based on music separation into harmonic and drum components. For this purpose, audio signal separation is executed to extend the overall vector of parameters by new descriptors extracted from harmonic and/or drum music content. The study is performed using the ISMIS database of music files represented by vectors of parameters containing music features. The Support Vector Machine (SVM) classifier and co-training method adapted for the standard SVM are involved in genre classification. Also, some additional experiments are performed using reduced feature vectors, which improved the overall result. Finally, results and conclusions drawn from the study are presented, and suggestions for further work are outlined.