Paper Reading AI Learner

Data in the Life: Authorship Attribution of Lennon-McCartney Songs

2019-06-12 23:52:05
Mark E. Glickman, Jason I. Brown, Ryan B. Song

Abstract

The songwriting duo of John Lennon and Paul McCartney, the two founding members of the Beatles, composed some of the most popular and memorable songs of the last century. Despite having authored songs under the joint credit agreement of Lennon-McCartney, it is well-documented that most of their songs or portions of songs were primarily written by exactly one of the two. Furthermore, the authorship of some Lennon-McCartney songs is in dispute, with the recollections of authorship based on previous interviews with Lennon and McCartney in conflict. For Lennon-McCartney songs of known and unknown authorship written and recorded over the period 1962-66, we extracted musical features from each song or song portion. These features consist of the occurrence of melodic notes, chords, melodic note pairs, chord change pairs, and four-note melody contours. We developed a prediction model based on variable screening followed by logistic regression with elastic net regularization. Out-of-sample classification accuracy for songs with known authorship was 76\%, with a $c$-statistic from an ROC analysis of 83.7\%. We applied our model to the prediction of songs and song portions with unknown or disputed authorship.

Abstract (translated)

披头士乐队的两位创始成员约翰·列侬和保罗·麦卡特尼的歌曲创作组合,创作了上个世纪最受欢迎和最令人难忘的歌曲。尽管根据Lennon McCartney的联合信贷协议创作了歌曲,但有充分的证据表明,他们的大部分歌曲或歌曲的一部分主要是由两人中的一人创作的。此外,一些列侬·麦卡特尼歌曲的作者身份也存在争议,根据之前对列侬和麦卡特尼的访谈,人们对作者身份的回忆也存在冲突。对于在1962-66年间创作和录制的已知和未知作者的列侬·麦卡特尼歌曲,我们从每首歌曲或歌曲部分中提取音乐特征。这些特征包括旋律音符、和弦、旋律音符对、和弦变换对和四音符旋律轮廓的出现。我们建立了一个基于变量筛选的预测模型,然后用弹性网络正则化进行逻辑回归。已知作者歌曲的样本分类准确率为76%,ROC分析的统计数据为83.7%。我们将我们的模型应用于对作者身份不明或有争议的歌曲和歌曲部分的预测。

URL

https://arxiv.org/abs/1906.05427

PDF

https://arxiv.org/pdf/1906.05427.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot