Indian Art Music Raga Recognition Dataset (features)

The Rāga Recognition Datasets (features) comprise two sizable datasets, one for each music tradition: the Carnatic Music Dataset (CMD) and the Hindustani Music Dataset (HMD). Each dataset entry includes features such as pitch, tonic, and nyas and tani segments. These datasets can be used to develop and evaluate approaches for automatic rāga recognition in Indian art music. To the best of our knowledge, they are the largest and most comprehensive datasets (in terms of available metadata) ever used for studying this task.

This repository only contains the metadata and computed features for the dataset, and shared in open access. To get the audio, please refer to this zenodo entry and submit your request.

Please cite the following publications if you use the material shared here in your research work.

Gulati, S., Serrà, J., Ganguli, K. K., ¸Sentürk, S., & Serra, X. (2016). Time-delayed melody surfaces for raga recognition. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), pp. 751–757. New York, USA. [Postprint PDF]

Gulati, S., Serrà, J., Ishwar, V., ¸Sentürk, S., & Serra, X. (2016). Phrase-based raga recognition using vector space modeling. In Proceedings of the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 66–70. Shanghai, China. [Postprint PDF]

Annotation Format

We provide both tsv files and json files that contain information about each audio recording in terms of its mbid, the path of the audio/feature files and the associated rāga identifier. Each rāga is assigned a unique identifier by Dunya, which is similar to the mbid in terms of purpose. We also provide a mapping of the rāga id to its transliterated name.

Mirdata

This dataset is included in mirdata. Use the following code snippet to access the dataset in mirdata.

# Import midata
import mirdata

# Initialize dataset
dataset_name = 'compmusic_raga'
data_home = 'mirdata/dataset'
dataset = mirdata.initialize(dataset_name, data_home=data_home)

# Download dataset
dataset.download()

# Validate dataset
dataset.validate()

# Load dataset as a dictionary with track ids as keys and track objects as values
data = dataset.load_tracks()

In order to load the audio files in mirdata, they must be requested beforehand and placed in the data home directory.

Contact 

If you have any questions or comments about the dataset, please feel free to email:

[e-mail]

拉格（Rāga）识别特征数据集包含两个大型数据集，分别对应两大印度古典音乐流派：卡纳提克音乐数据集（Carnatic Music Dataset, CMD）与印度斯坦尼音乐数据集（Hindustani Music Dataset, HMD）。每条数据集条目包含音高、主音（tonic）、尼亚斯（nyas）与塔尼（tani）片段等特征。本数据集可用于开发与评估印度艺术音乐的自动拉格识别方法。据我们所知，这是目前用于该任务研究的规模最大、元数据最全面的数据集。

本仓库仅包含该数据集的元数据与计算得到的特征，并以开放获取形式共享。若需获取音频文件，请参阅该Zenodo条目并提交申请。

若您在研究工作中使用此处共享的素材，请引用以下出版物。

Gulati, S., Serrà, J., Ganguli, K. K., ¸Sentürk, S., & Serra, X. (2016). 时延旋律表面用于拉格识别。见第17届国际音乐信息检索学会大会（ISMIR）论文集，第751–757页。美国纽约。[预印本PDF]

Gulati, S., Serrà, J., Ishwar, V., ¸Sentürk, S., & Serra, X. (2016). 基于向量空间建模的乐句拉格识别。见第41届IEEE国际声学、语音与信号处理大会（ICASSP）论文集，第66–70页。中国上海。[预印本PDF]

## 注释格式
我们提供TSV（Tab-Separated Values）文件与JSON文件，其中包含每条音频录制的相关信息，包括其MBID（MusicBrainz Identifier）、音频/特征文件的路径，以及关联的拉格标识符。Dunya平台为每个拉格分配了唯一标识符，其用途与MBID类似。我们还提供了拉格ID到其音译名称的映射表。

## Mirdata库
本数据集已纳入mirdata库。可使用以下代码片段访问该数据集：

# Import mirdata
import mirdata

# Initialize dataset
dataset_name = 'compmusic_raga'
data_home = 'mirdata/dataset'
dataset = mirdata.initialize(dataset_name, data_home=data_home)

# Download dataset
dataset.download()

# Validate dataset
dataset.validate()

# Load dataset as a dictionary with track ids as keys and track objects as values
data = dataset.load_tracks()

若需通过mirdata加载音频文件，需提前申请并将音频文件放置于data_home指定的目录中。

## 联系方式
若您对本数据集有任何疑问或建议，请通过以下邮箱联系：
[e-mail]