Analysis, Recognition, Manipulation and Generation of Music Signal and Information based on Mathematical Models

在音乐信号和信息的分析、识别、处理和生成方面，人们积极探索了一个多元化的研究领域，并取得了许多优秀的成果。其中包括相当广泛的研究成果，如基于多个并发音乐声音模型的多音高估计方法（称为HTC）和基于非负矩阵分解（NMF）的另一种方法，谐波-打击声分离（HPSS），基于功率谱相位分量恢复的高质量信号处理（例如音乐节奏和音高修改），立体声音乐录音的部分分离，基于人声谱波动的合奏音乐信号的人声提取与删除，基于音乐信号的和弦估计，音乐信号中组成节奏的自动提取（RhythmMap）与条形线分割，基于前述方法的音乐体裁与情绪识别，复调音乐信号中的节奏估计及其在音乐自动转写中的应用，基于动态贝叶斯网络的音乐建模和自动转写，将音乐理论和信号观察的层次结合起来，从乐谱数据中识别作曲家，利用条件随机场自动演奏音乐，模仿音乐动机自动对位，基于日语歌词固有音准的自动作曲，以及构建功能性和声数据库。这些成果通过期刊论文、国际会议、评论论文、国内学术报告、自动作曲网络服务和公共媒体等方式向公众公布。

An diverse research area has been actively investigated in analysis, recognition, manipulation and generation of music signal and information resulting in numerous excellent results. They include quite a wide range of research achievements such as multipitch estimation method (called HTC) based on a model of multiple concurrent music sounds and another method based on non-negative matrix factorization (NMF), harmonic-percussive sound separation (HPSS), high-quality signal manipulation (e.g., music tempo and pitch modification) based on restoration of phase components from power spectrogram, part separation form stereophonic music recordings, vocal sound extraction and deletion from ensemble music signals based on spectral fluctuation of human voice, chord estimation from music signals, automatic extraction of constituent rhythms (RhythmMap) in music signals and bar-line segmentation, music genre and mood recognition based on the previous method, rhythm estimation in polyphonic music signals and application to automatic music transcription, music modeling and automatic transcription based on dynamic Bayesian network to integrate hierarchical layers from music theory to signal observations, composer identification from score data, automatic music performance rendition using conditional random field, automatic counterpoint with imitation of music motives, automatic song composition based on intrinsic intonation of Japanese lyrics, and building functional harmony database. These achievements have been made public through journal papers, international conferences, review papers, domestic academic presentations, web service for automatic music composition, and public media.