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 producer effect on classification performance and demonstrate the trade-offs between audio 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. Additionally, to showcase the effectiveness of the CRNN's feature extraction capabilities, we visualize audio samples at the model's bottleneck layer demonstrating that learned representations segment into clusters belonging to their respective artists.
Advances in artist classification with machine learning are limited. Furthermore, while exploratory attempts exist @cite_4 , a comprehensive study with deep learning is still absent. Prior academic works are almost a decade old and employ traditional algorithms which do not work well with high-dimensional and sequential data. @cite_19 , for example, use a one second MFCC feature representation and a Support Vector Machine (SVM) classification model to achieve a best test accuracy of 50 Addressing this issue alongside the release of the dataset, Labrosa uses a full-covariance Gaussian classifier to establish an artist classification baseline @cite_20 with an album split. Using randomly sampled MFCC vectors from each artist, the model achieves 56 Mandel and Ellis @cite_11 study the impact of frame level versus song level evaluation using Gaussian Mixture Models (GMM) and SVMs. On the dataset, at the frame level, the GMM achieves comparable performance (54 All of the works discussed in this subsection are summarized in Table and used as baselines in this study.
@cite_19 @cite_4 @cite_20 @cite_11
1508121828 2140013053 2283723945 2134557905
Searching and organizing growing digital music collections requires automatic classification of music. This paper describes a new system, tested on the task of artist identification, that uses support vector machines to classify songs based on features calculated over their entire lengths. Since support vector machines are exemplarbased classifiers, training on and classifying entire songs instead of short-time features makes intuitive sense. On a dataset of 1200 pop songs performed by 18 artists, we show that this classifier outperforms similar classifiers that use only SVMs or song-level features. We also show that the KL divergence between single Gaussians and Mahalanobis distance between MFCC statistics vectors perform comparably when classifiers are trained and tested on separate albums, but KL divergence outperforms Mahalanobis distance when trained and tested on songs from the same albums. Multiple-instance learning algorithms train classifiers from lightly supervised data, i.e. labeled collections of items, rather than labeled items. We compare the multiple-instance learners mi-SVM and MILES on the task of classifying 10second song clips. These classifiers are trained on tags at the track, album, and artist levels, or granularities, that have been derived from tags at the clip granularity, allowing us to test the effectiveness of the learners at recovering the clip labeling in the training set and predicting the clip labeling for a held-out test set. We find that mi-SVM is better than a control at the recovery task on training clips, with an average classification accuracy as high as 87 over 43 tags; on test clips, it is comparable to the control with an average classification accuracy of up to 68 . MILES performed adequately on the recovery task, but poorly on the test clips. We propose a hierarchical approach to multi-action recognition that performs joint classification and segmentation. Ai¾źgiven video containing several consecutive actions is processed via a sequence of overlapping temporal windows. Each frame in a temporal window is represented through selective low-level spatio-temporal features which efficiently capture relevant local dynamics. Features from each window are represented as a Fisher vector, which captures first and second order statistics. Instead of directly classifying each Fisher vector, it is converted into a vector of class probabilities. The final classification decision for each frame is then obtained by integrating the class probabilities at the frame level, which exploits the overlapping of the temporal windows. Experiments were performed on two datasets: s-KTH ai¾źstitched version of the KTH dataset to simulate multi-actions, and the challenging CMU-MMAC dataset. On s-KTH, the proposed approach achieves an accuracy of 85.0i¾ź , significantly outperforming two recent approaches based on GMMs and HMMs which obtained 78.3i¾ź and 71.2i¾ź , respectively. On CMU-MMAC, the proposed approach achieves an accuracy of 40.9i¾ź , outperforming the GMM and HMM approaches which obtained 33.7i¾ź and 38.4i¾ź , respectively. Furthermore, the proposed system is on average 40 times faster than the GMM based approach. We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13 for SVM and 7 for convolutional nets. On a segmentation recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16 7 error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.