Omkar2118/datasets huggingface dataset:
Guzheng_Tech99

Instrument playing technique (IPT) is a key element of musical presentation.

Guzheng is a polyphonic instrument. In Guzheng performance, notes with different IPTs are usually overlapped and mixed IPTs that can be decomposed into multiple independent IPTs are usually used. Most existing work on IPT detection typically uses datasets with monophonic instrumental solo pieces. This dataset fills a gap in the research field.

The dataset comprises 99 Guzheng solo compositions, recorded by professionals in a studio, totaling 9064.6 seconds. It includes seven playing techniques labeled for each note (onset, offset, pitch, vibrato, point note, upward portamento, downward portamento, plucks, glissando, and tremolo), resulting in 63,352 annotated labels. The dataset is divided into 79, 10, and 10 songs for the training, validation, and test sets, respectively.

More details about the code and datasets can be found at https://lidcc.github.io/GuzhengTech99/

potential use cases: instrument playing technique detection, Guzheng transcription, multi-pitch estimation, note tracking, sound event detection…

Introducing paper: Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism
ESP Dataset
Full name: Evaluation for Styled Prompt datase

ESP dataset (Evaluation for Styled Prompt dataset) is a new benchmark for zero-shot domain-conditional caption generation. The dataset aims to evaluate the capability to generate diverse domain-specific language conditioned on the same image. It comprises 4.8k captions from 1k images in the COCO Captions test set. We collected five text domains with everyday usage: blog, social media, instruction, story, and news using Amazon MTurk.
VIGOR

Similar to CVUSA and CVACT, the VIGOR dataset contains satellites and street imagery to match them to each other to find the location of the street imagery. For this purpose, data from 4 major American cities were used, namely San Francisco, New York, Seattle and Chicago. Unlike the previous datasets, there are two settings: The SAME-Area setting where images of all cities are available in training and validation split. Secondly, there is the CROSS area setting where training is done on two cities (New York, Seattle) and evaluation is done on Chicago and San Francisco. In addition, the dataset contains semi-positive images which are very close to an actual ground truth image and thus serve as a distraction for the matching task. In total, the dataset consists of 90,618 satellite images and 105,214 street images.