Music Artist Classification with Convolutional Recurrent Neural Networks

Previous attempts at music artist classification use frame-level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions. These include audio clip length, which is a novel contribution in this work, and previously identified considerations such as dataset split and feature-level. Our results improve upon baseline works, verify the influence of the production details on classification performance and demonstrate the trade-offs between sample length and training set size. The best performing model achieves an average F1-score of 0.937 across three independent trials which is a substantial improvement over the corresponding baseline under similar conditions. Finally, to showcase the effectiveness of the CRNN's feature extraction capabilities, we visualize audio samples at its bottleneck layer demonstrating that learned representations segment into clusters belonging to their respective artists.

PyOD: A Python Toolbox for Scalable Outlier Detection

PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for use by both practitioners and researchers. With robustness and scalability in mind, best practices such as unit testing, continuous integration, code coverage, maintainability checks, interactive examples and parallelization are emphasized as core components in the toolbox's development. PyOD is compatible with both Python 2 and 3 and can be installed through Python Package Index (PyPI) or https://github.com/yzhao062/pyod.

LSCP: Locally Selective Combination in Parallel Outlier Ensembles

In unsupervised outlier ensembles, the absence of ground truth makes the combination of base detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we propose a framework---called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)---which addresses this issue by defining a local region around a test instance using the consensus of its nearest neighbors in randomly generated feature spaces. The top-performing base detectors in this local region are selected and combined as the model's final output. Four variants of the LSCP framework are compared with six widely used combination algorithms for parallel ensembles. Experimental results demonstrate that one of these LSCP variants consistently outperforms baseline algorithms on the majority of eighteen real-world datasets.