Georgia Tech音乐表演评价

Georgia Tech的音乐技术组还有三篇不用深度学习的音乐表演评价文章。都是在Florida Bandmasters Association dataset这个数据库上做的。前两篇十分类似，最后一篇用sparse coding来无监督学特征。

其实整个三篇都没什么亮点，review的目的是吃吃鸡肋。

Towards the Objective Assessment of Music Performances

ICMPC 2016

此文可以不用读，直接跳到后两篇就好。。。

Vidwans, Amruta

Gururani, Siddharth

Wu, Chih-Wei

Subramanian, Vinod

Swaminathan, Rupak Vignesh

Lerch, Alexander

Objective descriptors for the assessment of student music performances

http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2017/06/Vidwans-et-al_2017_Objective-descriptors-for-the-assessment-of-student-music-performances.pdf

用DTW对齐了pitch track和reference score，然后将pitch track切成了notes。提取的特征分两类，一个是score-based，一类是score independent。两类特征合起来用效果最好。

Using score-based features, DTW alignment the pitch track with the reference score helps segment the pitch track into notes.

Features:

(1) note steadiness

(2) duration histogram

(3) DTW based feature, cost normalized by the DTW length and slope deviation

(4) note insertion ratio

(5) note deletion ratio

score-based and score independent features are the best performed one.

Dataset:

A subset of 394 students in Florida Bandmasters Association dataset, three grades and 4 assessment dimensions.

Wu, Chih-Wei

Lerch, Alexander

Learned Features for the Assessment of Percussive Music Performances

http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2018/01/Wu_Lerch_2018_Learned-Features-for-the-Assessment-of-Percussive-Music-Performances.pdf

此文用Sparce coding无监督的方式来学习特征，用来评价打击乐的表演。

数据库：274首中学生的军鼓表演。评价指标是musicality和节奏准确性。

特征：

Local histogram matrix (LHM)。使用了三个特征IOI, amplitude, average MFCC。将一整首表演切成10秒的小段落，对每个段落计算histogram。然后将这三个特征的histogram在特征维度和时间唯独concatenate一下变成一个矩阵。

模型：Sparse coding从LHM学出来的特征加上SVR回归

结果：Sparse coding+LHM和直接从LHM里面计算的统计量两种特征的效果相当。把两个特征一起用效果更好。

Using sparse coding unsupervised learning to learn features for the percussive music performance.

Dataset: 274 recordings of middle school snare etudes. Assessment of musicality and rhythmic accuracy.

Features:

LHM local histogram matrix:

(1) IOI (inter-onset interval) histogram vector

(2) Amplitude histogram vector

(3) Average MFCC vector

They segment the whole music piece into non-overlapped 10s segments, compute the local histogram vectors of above three features and concatenate these vector in both feature and time dimensions.

Baseline:

(1) a bunch of features

(2) statistics of LHM features (crest, skewness, ...)

(3) Sparse code of STFT

Model:

SVR

Metrics:

correlation coef and coef of determination

Results:

Learned features (Sparse code with LHM) achieve comparable results with the designed features, Finally, combining the designed features with the SC features, the highest performance can be achieved.