发表机构
Music Minus One（音乐减一公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究钢琴家定制MMO协奏曲伴奏的方法，提出密集-稀疏DTW，通过使用三种音频数据，解决频谱不匹配问题，建立数据收集与评估框架，实验表明该方法在定制伴奏录音上比复杂方法性能更好或相当。
AI 中文摘要
在本研究中，我们探索钢琴家如何定制音乐减一（MMO）协奏曲伴奏以匹配他们的演奏风格。绕过通常没有数字形式的符号乐谱需求，我们使用三种类型的音频数据：独奏钢琴录音、仅MMO管弦乐队录音以及钢琴和管弦乐队的混合录音（例如来自YouTube）。混合录音作为中介参考来对齐独奏和管弦乐队部分，仅通过时间尺度修改调整管弦乐队部分以与用户演奏同步。估计这些对齐的主要挑战是包含不同音乐部分的录音之间的频谱不匹配。受此应用场景启发，我们引入密集-稀疏动态时间规整（Dense-Sparse DTW），它是动态时间规整（DTW）的一种变体，旨在通过专注于对齐包含突出时间线索的选定音频帧子集来提高对齐对频谱不匹配的鲁棒性。我们从四个钢琴协奏曲乐章收集并标注数据，建立了一个生成和评估定制伴奏录音的框架。在此基准上，我们表明密集-稀疏DTW比基于源分离和频谱减法技术的更复杂方法具有更好或相当的性能。
英文摘要
In this study, we explore how pianists can customize Music Minus One (MMO) concerto accompaniments to match their playing style. Bypassing the need for a symbolic score, often not available digitally, we use three types of audio data: solo piano recordings, MMO orchestra-only recordings, and mixed recordings of both piano and orchestra (e.g., from YouTube). The mixed recording serves as an intermediary reference to align the solo and orchestra parts, with only the orchestral part being adjusted through time-scale modification to synchronize with the user's playing. The main challenge with estimating these alignments is the spectral mismatch between recordings containing different musical parts. Motivated by this application scenario, we introduce Dense-Sparse DTW, a variant of Dynamic Time Warping (DTW) that is designed to improve robustness of alignments to spectral mismatch by focusing on aligning a selected subset of audio frames containing prominent timing cues. We collect and annotate data from four piano concerto movements and establish a framework for generating and evaluating customized accompaniment recordings. On this benchmark, we show that Dense-Sparse DTW has better or comparable performance than more complex approaches based on source separation and spectral subtraction techniques.
CommentsPublished at ICASSP 2025
Journal refProc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025, pp. 1-5
DOI：10.1109/ICASSP49660.2025.10890080
