Learning sparse dictionaries for music and speech classification

The field of music and speech classification is quite mature with researchers having settled on the approximate best discriminative representation. In this regard, Zubair et al. showed the use of sparse coefficients alongwith SVM to classify audio signals as music or speech to get a near-perfect classification. In the proposed method, we go one step further, instead of using the sparse coefficients with another classifier they are directly used in a dictionary which is learned using on-line dictionary learning for music-speech classification. This approach removes the redundancy of using a separate classifier but also produces complete discrimination of music and speech on the GTZAN music/speech dataset. Moreover, instead of the high-dimensional feature vector space which inherently leads to high computation time and complicated decision boundary calculation on the part of SVM, the restricted dictionary size with limited computation serves the same purpose

Learning sparse dictionaries for music and speech classification

The field of music and speech classification is quite\ud mature with researchers having settled on the approximate best\ud discriminative representation. In this regard, Zubair et al. showed\ud the use of sparse coefficients alongwith SVM to classify audio\ud signals as music or speech to get a near-perfect classification. In\ud the proposed method, we go one step further, instead of using\ud the sparse coefficients with another classifier they are directly\ud used in a dictionary which is learned using on-line dictionary\ud learning for music-speech classification. This approach removes\ud the redundancy of using a separate classifier but also produces\ud complete discrimination of music and speech on the GTZAN\ud music/speech dataset. Moreover, instead of the high-dimensional\ud feature vector space which inherently leads to high computation\ud time and complicated decision boundary calculation on the part\ud of SVM, the restricted dictionary size with limited computation\ud serves the same purpose

Pre-trained Deep Neural Network using Sparse Autoencoders and Scattering Wavelet Transform for Musical Genre Recognition

Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scattering Wavelet Transform (SWT) for classifying musical genres. The SWT uses a sequence of Wavelet Transforms to compute the modulation spectrum coefficients of multiple orders, which has already shown to be promising for this task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is used to boost the well-known GTZAN database, which is a standard benchmark for this task. The final classifier is tested using a 10-fold cross validation to achieve results similar to other state-of-the-art approaches