Resultados de investigación

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 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 dataset contains 23 multimodal recordings of string quartet performance. Acquired data includes the audio of each performance in separate tracks: up to two ambient tracks and individual tracks acquired through contact microphones. In addition each recording includes instrumental motion capture and a set of derived instrumental bowing descriptors for each musician. The recordings were made as part of the experiments on ensemble expressive performance.

El zip conté tots els recursos que s'han generat durant el desenvolupament de la tesi. Per una banda, hi ha el codi, amb el qual es poden extreure el conjunt de features tal i com es descriu a la tesi, per altre banda, hi ha també tots els datasets que s'han creat i que s'utilitzen per a tots els experiments. Utilitzant el codi, les eines externes corresponents i els datasets, es poden emular tots els experiments descrits. The zip file contains every resource that has been generated during the development of the thesis. One of the folders contains the code that is used to extract the described feature set, the other one contains every dataset that has been compiled and used in every experiment. Using the code, the external tools mentioned in the experiments and the corpora, it is possible to repeat every experiment described in the thesis.

Infants' raw eye-tracker data used in the study presented at the paper entitled "Infants' representation of social hierarchies in absence of physical dominance"

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Punctuation marks support understandability and readability in written language. In spoken language, punctuation of the transcribed speech is influenced by two phenomena: (1) syntax and (2) prosody. We present a software architecture that makes it possible to train punctuation restoration models from any combination of lexical, morphosyntactic, prosodic and acoustic features. Architecture is language independent and feeds on word-segmented data. A dataset compiled from English TED talks is given in http://hdl.handle.net/10230/33981