Music Information Processing Using Continuous Speech Recognition Methods

使用连续语音识别方法的音乐信息处理

• 批准号: 14380156
• 负责人: SAGAYAMA Shigeki
• 金额: $ 10.82万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2002
• 资助国家: 日本
• 起止时间: 2002 至 2004
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-14380156/
• 关键词: continuous speech recognition; music information processing; rhythm recognition; automatic harmonization; automatic counterpoint; harmonic clustering; specmurt; 確率モデル; 時空間クラスタリング; Specmurt法; 自動和声づけ; 自動採譜; MIDI信号; 連続音声認識手法; リズムベクトル; 隠れマルコフモデ

We formulated music rhythm recognition for ranscribing MIDI data into music score as a Viterbi path search problem in HMM where hidden states and output probabilities represent the intended note values and actually played note lengths, respectively. We also solved rhythm recognition of polyphonic music by reducing polyphony intomonophony. Tempo modeling and tempo change detection were enabled with segmental k-means algorithm for speech recognition.Harmonization (chord finding) of given melodies was formulated as an isomorphic problem as continuous speech recognition by defining output by the given melody, hidden states by the chord behind the melody and stochastic language model by chord sequences. Automatic counterpoint was developed with a two-step maximum likelihood approach consisting of rhythm design and pitch allocation solved by dynamic programming.In polyphonic signal analysis, an algorithm named Harmonic-structured Clustering was developed based on the k-means clustering algorithm under harmonic constraint by modeling the framewise observed spectrum as overlapped harmonic structures and considering that the distributed energy in harmonic structure belongs to a single cluster. Furthermore, by introducing the probabilistic assignment to clusters, k-means was generalized into the EM-algorithm and attained higher performance of multi-pitch estimation. Utilizing an information criterion such as AIC, the number of sources and octave location were also enabled."Specmurt analysis" was proposed for polyphonic signal analysis. The inverse Fourier transform of linear spectrum with log-frequency was called "specmurt". Along log-scaled frequency, observed linear spectrum is regarded as convolution of distribution density of fundamental frequencies and harmonic structures of multiple tones which are assumed identical. This idea opened up a new signal processing capabilities.

我们为将MIDI数据称为hmm中的Viterbi路径搜索问题而制定了音乐节奏识别，其中隐藏的状态和输出概率代表了预期的注释值，并且分别播放了音符长度。我们还通过减少多相插曲来解决对复音音乐的识别。使用节度k-均值算法进行语音识别算法实现了节奏建模和速度变化检测。给定旋律的harmonization（和弦发现）被作为一个同构问题，作为同构问题作为连续的语音识别，通过通过旋律和结式模型在旋律和固定语言模型背后通过旋律的隐藏状态定义给定的旋律来识别持续的语音识别。自动对位的开发是通过动态编程通过节奏设计和俯仰分配组成的两步​​最大似然方法开发的。在多音信号分析中，一种算法的算法是基于k-means群集群集算法在谐波中构建型号的单个型号的谐波结构，并考虑了谐波构造的谐波，并考虑了谐波构造的谐波，并考虑了谐波构造的谐波，并考虑了谐波构造的谐波，并在谐波中建立了谐波，并在谐波上进行了谐波，并考虑了谐波构造的谐波，并将其视为谐波结构。 簇。此外，通过将概率分配引入簇，K-均值被推广到EM-Algorithm中，并获得了更高的多核估计效果。还启用了使用信息标准，例如AIC，源和八度位置的数量。“ SpecMurt Analysis”提出了用于多音信号分析的“ SpecMurt Analysis”。线性频谱的逆傅立叶变换使用对数频率称为“ SpecMurt”。沿着对数尺度的频率，观察到的线性光谱被认为是假定相同的多种音调的基本频率和谐波结构的分布密度的卷积。这个想法打开了新的信号处理功能。

项目成果

Rhythm Recognition of Multiphonic MIDI Signals Using Probabilistic Models

使用概率模型识别多音 MIDI 信号的节奏

• 发表时间: 2004
• 期刊: IPSJ Journal Vol.45, No.3
• 作者: Haruto Takeda;Takuya Nishimoto;Shigeki Sagayama
• 通讯作者: Shigeki Sagayama

リズム語彙を用いたHMMによるMIDI演奏のリズムとテンポ推定

使用节奏词汇使用 HMM 进行 MIDI 演奏的节奏和节奏估计

• 发表时间: 2004
• 期刊: 情報処理学会研究報告 2004-MUS-54
• 作者: 武田晴登;西本卓也;嵯峨山茂樹
• 通讯作者: 嵯峨山茂樹

調波スペクトル分離の原理Harmonic Clusteringと赤池情報量規準による多声部楽曲音響信号の同時発音数および多重ピッチの推定

使用谐波聚类、谐波频谱分离原理和 Akaike 信息准则估计复调音乐音频信号的同时语音数量和多个音高

• 发表时间: 2004
• 期刊: 日本音響学会7月音楽音響研究会資料
• 作者: 亀岡弘和;西本卓也;嵯峨山茂樹
• 通讯作者: 嵯峨山茂樹

Extraction of Multiple Fundamental Frequencies from Polyphonic Music Using Harmonic Clustering

使用谐波聚类从复调音乐中提取多个基本频率

• 发表时间: 2004
• 期刊: Proc. International Congress on Acoustics (ICA) (Kyoto, Japan)
• 作者: Hirokazu Kameoka;Takuya Nishimoto;Shigeki Sagayama
• 通讯作者: Shigeki Sagayama

Time-Space Clustering for Multi-pitch Spectral Segregation Using Kernel Audio Stream Model

使用内核音频流模型进行多音高频谱分离的时空聚类

• 发表时间: 2005
• 期刊: The 2005 Spring Meeting of the Acoustic Society of Japan 3-7-19
• 作者: Hirokazu Kameoka;Takuya Nishimoto;Shigeki Sagayama
• 通讯作者: Shigeki Sagayama