Wavelet-filtering of symbolic music representations for folk tune segmentation and classification

The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestaltbased method. Melodies are represented as discrete symbolic pitch-time signals. We apply the continuous wavelet transform (CWT) with the Haar wavelet at specific scales, obtaining filtered versions of melodies emphasizing their information at particular time-scales. We use the filtered signal for representation and segmentation, using the wavelet coefficients ’ local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet based segmentation and waveletfiltering of the pitch signal lead to better classification accuracy in cross-validated evaluation when the time-scale and other parameters are optimized. 1

Convolution-based classification of audio and symbolic representations of music

We present a novel convolution-based method for classification of audio and symbolic representations of music, which we apply to classification of music by style. Pieces of music are first sampled to pitch–time representations (piano-rolls or spectrograms) and then convolved with a Gaussian filter, before being classified by a support vector machine or by k-nearest neighbours in an ensemble of classifiers. On the well-studied task of discriminating between string quartet movements by Haydn and Mozart, we obtain accuracies that equal the state of the art on two data-sets. However, in multi-class composer identification, methods specialised for classifying symbolic representations of music are more effective. We also performed experiments on symbolic representations, synthetic audio and two different recordings of The Well-Tempered Clavier by J. S. Bach to study the method’s capacity to distinguish preludes from fugues. Our experimental results show that our approach performs similarly on symbolic representations, synthetic audio and audio recordings, setting our method apart from most previous studies that have been designed for use with either audio or symbolic data, but not both

Composer Recognition based on 2D-Filtered Piano-Rolls

[TODO] Add abstract here

Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection

This paper evaluates XGboost's performance given different dataset sizes and class distributions, from perfectly balanced to highly imbalanced. XGBoost has been selected for evaluation, as it stands out in several benchmarks due to its detection performance and speed. After introducing the problem of fraud detection, the paper reviews evaluation metrics for detection systems or binary classifiers, and illustrates with examples how different metrics work for balanced and imbalanced datasets. Then, it examines the principles of XGBoost. It proposes a pipeline for data preparation and compares a Vanilla XGBoost against a random search-tuned XGBoost. Random search fine-tuning provides consistent improvement for large datasets of 100 thousand samples, not so for medium and small datasets of 10 and 1 thousand samples, respectively. Besides, as expected, XGBoost recognition performance improves as more data is available, and deteriorates detection performance as the datasets become more imbalanced. Tests on distributions with 50, 45, 25, and 5 percent positive samples show that the largest drop in detection performance occurs for the distribution with only 5 percent positive samples. Sampling to balance the training set does not provide consistent improvement. Therefore, future work will include a systematic study of different techniques to deal with data imbalance and evaluating other approaches, including graphs, autoencoders, and generative adversarial methods, to deal with the lack of labels.Comment: 17 pages, 8 figures, 9 tables, Presented at NVIDIA GTC, The Conference for the Era of AI and the Metaverse, March 23, 2023. [S51129

An approach to melodic segmentation and classification based on filtering with the Haar wavelet

We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on multiple features