國際知識發現與資料探勘大會（ACM SIGKDD Conference on Knowledge Discovery and Data Mining,簡稱SIGKDD）是資料探勘領域的頂級國際會議。我們將持續對近年KDD的部分論文進行解讀。
KDD 2018共收到投稿論文1479篇，其中研究性論文983篇，應用資料科學論文496篇，均創下新高。本文選取了KDD 2018最佳學生論文獎獲獎論文進行介紹：
論文題目
XiaoIce Band:A Melody and Arrangement Generation Framework for Pop Music
作者
Hongyuan Zhu、Qi Liu、Nicholas Jing Yuan、Chuan Qin、Jiawei Li、Kun Zhang、Guang Zhou、Furu Wei、Yuanchun Xu、Enhong Chen
會議／年份
SIGKDD 2018
連結（點選閱讀原文也可獲取）
https://www.aminer.cn/archive/xiaoice-band-a-melody-and arrangement-generation-framework-for-pop music/5b67b45517c44aac1c8607e9
Abstract
With the development of knowledge of music composition and the recent increase in demand, an increasing number of companies and research institutes have begun to study the automatic generation of music. However, previous models have limitations when applying to song generation, which requires both the melody and arrangement. Besides, many critical factors related to the quality of a song such as chord progression and rhythm patterns are not well addressed. In particular, the problem of how to ensure the harmony of multi-track music is still underexplored. To this end, we present a focused study on pop music generation, in which we take both chord and rhythm influence of melody generation and the harmony of music arrangement into consideration. We propose an end-to-end melody and arrangement generation framework, called XiaoIce Band, which generates a melody track with several accompany tracks played by several types of instruments. Specifically, we devise a Chord based Rhythm and Melody Cross-Generation Model (CRMCG) to generate melody with chord progressions. Then, we propose a Multi-Instrument Co-Arrangement Model (MICA) using multi-task learning for multi-track music arrangement. Finally, we conduct extensive experiments on a real-world dataset, where the results demonstrate the effectiveness of XiaoIce Band.
摘要
音樂對人們的生活有著重要的影響。然而，創作音樂需要大量的專業知識和技能。近年來，如何利用機器學習技術自動進行音樂創作成為人工智慧領域的熱門話題。由於音樂元素的複雜性，如歌曲不同的和絃進行、樂段中結構鮮明的節奏型、不同特性的音軌（樂器）需要保持和諧一致等，使得高質量的單音軌作曲、多音軌編曲演算法的設計充滿了挑戰性與特殊性。為此，論文基於深度神經網路和多工學習等方法，從歷史音樂資料（如十萬多首歌曲）中學習音樂的音程關係、結構以及各種樂器的演繹特色，設計了一種基於和絃的節奏和旋律交叉的生成模型（CRMCG）來產生帶有和絃進行的旋律；更進一步，通過構建多個任務（即多個音軌，樂器序列）關聯模型，為樂器的相互配合搭建了資訊互動的橋樑，實現了一種多樂器聯合編曲模型(MICA)。
