BUSCADOR RECOLECTA

Oceanographic data acquired during the ABRIC-4 Cruise (29GD20230310) on board the Research Vessel García del Cid in 2023., Assessment of Bottom‐trawling Resuspension Impacts in deep benthic Communities (ABRIC)

Oceanographic data acquired during the ARCO Cruise (29GD20230626) on board the Research Vessel García del Cid in 2023., Assessment of fishing grounds Restoration after a Change of bottom trawling Otter boards (ARCO). The main objective of the ARCO project is to study the current sedimentary processes related with trawling activity in the Palamós submarine canyon after the change of trawl doors by the Palamós harbor fleet. The impacts of this type of fishing in deep-sea environments are considered very high due to their intensity and recurrence, and to the fact that sediment dynamics are less energetic and the benthonic ecosystems are more vulnerable and have less resilience than in neritic or shelf environments

-Dataset-/n/n The dataset is a collection of 133 audio percussion patterns spanning five different pattern classes as described below. The scores for the patterns and additional details about the patterns are at: http://compmusic.upf.edu/bo-perc-patterns/n/n-Audio Content-/n/nThe audio files are short segments containing one of the above mentioned patterns. The audio is stereo, sampled at 44.1 kHz, and stored as wav files. The segments were chosen from the introductory parts of arias. The recordings of arias are from commercially available releases spanning various artists. The audio and segments were chosen carefully by a musicologist to be representative of the percussion patterns that occur in Jingju. The audio segments contain diverse instrument timbres of percussion instruments (though the same set of instruments are played, there can be slight variations in the individual instruments across different ensembles), recording quality and period of the recording. Though these recordings were chosen from introductions of arias where only percussion ensemble is playing, there are some examples in the dataset where the melodic accompaniment starts before the percussion pattern ends. /n/n-Annotations-/n/nEach of the audio patterns has an associated syllable level transcription of the audio pattern. The transcription is obtained from the score for the pattern and is not time aligned to the audio. The transcription is done using the reduced set of five syllables described in Table 1 of [1] and is sufficient to computationally model the timbres of all the syllables. The annotations are stored as Hidden Markov Model Toolkit (HTK) label files. There is also a single master label file provided for batch processing using HTK (http://htk.eng.cam.ac.uk/). /n/n-Dataset organization-/n/nThe dataset has wav files and label files. The files are named as /n./nThe pID is as in Table 1, instID is a three digit identifier for the specific instance of the pattern, and extension can be .wav for the audio file or .lab for the label file. pID ϵ {10, 11, 12, 13, 14}, InstID ϵ {1, 2, ..., NpID}. e.g. The audio file and the label file for the fifth instance of the pattern duotuo is named 12005.wav and 12005.lab, respectively. The master label file is called masterLabels.lab/n/n-Availability of the Dataset-/n/n The annotations are publicly shared and available to all. The audio is from commercially available releases. It cannot be publicly shared but can be made available on request for non-commercial research purposes. In the future, the dataset would be available for viewing and download through an interface in Dunya (http://dunya.compmusic.upf.edu). Beijing Opera Percussion Pattern (BOPP) dataset is a collection of 133 audio percussion patterns covering five pattern classes. The dataset includes the audio and syllable level transcriptions for the patterns (non-time aligned). It is useful for percussion transcription and classification tasks. The patterns have been extracted from audio recordings of arias and labeled by a musicologist.

Beijing Opera percussion dataset is a collection of 236 examples of isolated strokes spanning the four percussion instrument classes used in Beijing Opera. It can be used to build stroke models for each percussion instrument. /nAll the sounds in this pack were played by Ying Wan of the London Jing Kun Opera Association. Recorded by Mi Tian at the Centre for Digital Music, Queen Mary University of London, UK in September 2013 using an AKG C414 microphone under studio conditions.

This study presents a large machine-readable dataset of Turkish makam music scores segmented into phrases by experts of this music. The segmentation facilitates computational research on melodic similarity between phrases, and relation between melodic phrasing and meter, rarely studied topics due to unavailability of data resources.

Turkish şarkı vocal dataset is a collection of recordings of compositions from the vocal form şarkı. The recordings are selected from a musicBrainz collection of Turkish music: /n/nhttp://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb. /n/n The collection has annotations with lyrics. Each lyrical phrase is aligned to its corresponding segment in the audio. It features 10 performances of different compositions. Five are sung by male and 5 by female singer. The recordings are selected so that there is only a single main vocalist (or the intensity of the back vocalst is relatively low compared to the main vocalist). Accompanying instruments are mainly string ensembles. No percussive instruments are present. Audio is in .wav format./n- Lyrical phrases annotations-/n /nThe audio is segmented into one-section chunks (a section is nakarat, meyan etc.)/nEach audio segment is aligned to the lyrical phrases. A phrase corresponds roughly to a musical bar and contains 1 or 2 words. /n/nAn annotation file is in .TextGrid format of Praat.

Turkish şarkı vocal dataset is a collection of recordings of compositions from the vocal form şarkı. The recordings/nare selected from a musicBrainz collection of Turkish music/n/nhttp://musicbrainz.org/collection/544f7aec-dba6-440c-943f-103cf344efbb /n/nThe collection has annotations with lyrics. Each lyrical phrase is aligned to its corresponding segment in the audio.

Turkish makam acapella sections dataset is sung by professional singers and is a collection of recordings of compositions from the vocal form ?ark?. They are selected to be the same as the recordings in version two of http://compmusic.upf.edu/turkish-sarki/nThe main intention is to provide acapella counterpart to polyphonic recordings.

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.