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This release contains 2095 sections annotated in 257 audio recordings of 58 compositions. The midi and SymbTr-scores of the compositions are also included in the dataset. The section test dataset for classical Ottoman-Turkish makam music. This repository contains the audio section annotations and the scores used in the paper: > Şentürk, S., Holzapfel, A., and Serra, X. (2014). Linking scores and audio recordings in makam music of Turkey. Journal of New Music Research, 43:34–52. Please cite the publication above in any work using this dataset. For more information about the dataset, please refer to the paper. Remarks: - If an audio recording is related to multiple works (e.g. multiple compositions are performed in the recording), in the audio_metadata we only keep the work of the score. - The "title" fields in the audio_metadata might have unicode characters. - In the original symbTr format the sections are given in the "Soz1" (Lyrics1) field. This field is used for the lyrics of the vocal compositions and the sections of the instrumental compositions. We created another field called "Bolum1" (Section1) for the dedicated to sections. In the instrumental compositions, we copied the section names already given in the "Soz1" field and manually corrected any erroneous jumps. For the vocal compositions, we manually labeled the score sections from the repetitions in the the lyrics and the melody. In the future we plan to automate the extraction from the repetition of the symbolic lyrics and melody repetitions.

This release contains annotated tonic frequencies of 257 audio recordings. The SymbTr-scores of the corresponding compositions performed in the audio recordings are also indicated.

This release contains annotated tonic frequencies of 1093 audio recordings in total.

This repository contains datasets of annotated tonic frequencies of the audio recordings of Ottoman-Turkish makam music. The annotations are compiled from several research papers published under the CompMusic project. For more information about the original datasets, please refer to the relevant paper.

The repository includes the test datasets used in various audio-score alignment experiments on Ottoman-Turkish makam music. This particular release contains the audio-score alignment test dataset used in the paper: Şentürk, S., Gulati, S., and Serra, X. (2014). Towards alignment of score and audio recordings of Ottoman-Turkish makam music. In Proceedings of 4th International Workshop on Folk Music Analysis, pages 57–60, Istanbul, Turkey.

This dataset corresponds to a study aimed at investigating the environmental impact of an innovative system proposed for residential buildings in Mediterranean climate through a life cycle assessment. The data refer to both the innovative system and a standard reference system, which was considered for comparison purposes. Data used for a parametric study of the innovative system are also included in this dataset.

The Mridangam Stroke dataset is a collection of 7162 audio examples of individual strokes of the Mridangam in various tonics. The dataset comprises of 10 different strokes played on Mridangams with 6 different tonic values. The dataset can be used for training models for each Mridangam stroke.

Audio music content----- They feature 7 varnams in 7 raagas sung by 5 young professional singers who received training for more than 15 years. They are all set to Adi taala. Measuring the intonation variations require absolutely clean pitch contours. For this, all the varṇaṁs are recorded without accompanying instruments, except the drone. Taala annotations----- The recordings are annotated with taala cycles, each annotation marking the starting of a cycle. We have later automatically divided each cycle into 8 equal parts. The annotations are made available as sonic visualizer annotation layers. Each annotation is of the format m.n where m is the cycle number and n is the division within the cycle. All m.1 annotations are manually done, whereas m.[2-8] are automatically labelled. Notations----- The notations for 7 varnams are procured from an archive curated by Shivkumar, in word document format. They are manually converted to a machine readable format (yaml). Each file is essentially a dictionary with section names of the composition as keys. Each section is represented as a list of cycles. Each cycle in turn has a list of divisions. Possible uses of the dataset----- The distinct advantage of this dataset is the free availability of the audio content. Along with the annotations, it can be used for melodic analyses: characterizing intonation, motif discovery and tonic identification. The availability of a machine readable notation files allows the dataset to be used for audio-score alignment.

These datasets comprise audio excerpts and manually done annotations of the tonic pitch of the lead artist for each audio excerpt. Each excerpt is accompanied by its associated editorial metadata. These datasets can be used to develop and evaluate computational approaches for automatic tonic identification in Indian art music. These datasets have been used in several articles mentioned below. A majority of these datasets come from the CompMusic corpora of Indian art music, for which each recording is associated with a MBID. With the MBID other information can be obtained using the Dunya API. We here provide an overview of the tonic identification datasets. Datasets ------- The statistics about the datasets for tonic identification is listed in the table below. These six datasets are used in Gulati, S., Bellur, A., Salamon, J., Ranjani, H. G., Ishwar, V., Murthy, H. A., & Serra, X. (2014). Automatic Tonic Identification in Indian Art Music: Approaches and Evaluation. Journal of New Music Research, 43(01), 55–73 for a comparative evaluation. To the best of our knowledge these are the largest datasets available for tonic identification for Indian art music. These datases vary in terms of the audio quality, recording period (decade), the number of recordings for Carnatic, Hindustani, male and female singers and instrumental and vocal excerpts. For a detailed information about these datasets we refer to Chapter 3 of this thesis (http://hdl.handle.net/10803/398984). The audio files corresponding to these datsets are made available on request for only research purposes. To obtain the files fill the FORM (https://goo.gl/forms/kWzpCsZW8DM7noW63). CompMusic Tonic Identification Datasets --- Datasets: CM1, CM2, CM3 Features: pitch + multipitch histogram + pitch histograms IITM Tonic Identification Datasets Datasets: IITM1, IITM2 Features: pitch + multipitch histogram + pitch histograms IISc Tonic identification Dataset Dataset: IISc Features: pitch + multipitch histogram + pitch histograms Annotation Format ---The tonic annotations are availabe both in tsv and json format. TSV: relative path to audio, tonic (Hz), Carnatic or Hindustani, artist_name, gender of the singer, vocal or instrumental JSON: name of the lead artist if available, 'filepath': relative path to the audio file, gender of the lead singer if available, 'mbid': musicbrainz id when available, 'tonic': tonic in Hz, 'tradition': Hindustani or Carnatic, 'type': vocal or instrumental where keys of the main dictionary are the filepaths to the audio files (feature path is exactly the same with a different extension of the file name).

The effect of different dietary composition on liver lipid accumulation was analyzed. Diets with the same caloric content were given, but which differed in the percentage content (both in weight and derived energy) of proteins and lipids: Control diet (19% protein + 12.5% ​​lipids + 67% carbohydrates), Cafeteria diet (11% protein + 39% fat + 48% carbohydrates), Hyperlipidic diet (HF) (14% protein + 37% fat + 48% carbohydrates), hyperprotein diet (HP) (40% protein + 12% fat + 47% carbohydrates). Cafeteria and HL diets, despite having the same lipid content, differed in their lipid composition. Animals were treated for 30 days with these diets and intake was assessed. Blood and tissue samples were taken after the animals were sacrificed. The usual plasma parameters were measured and in this case, the content of cholesterol and triacylglycerols was determined in the liver, as well as the expression (RNA) of different enzymes involved in its metabolism.