It is very important to access a rich music dataset that is useful in a wide
variety of applications. Currently, available datasets are mostly focused on
storing vocal or instrumental recording data and ignoring the requirement of
its visual representation and retrieval. This paper attempts to build an
XML-based public dataset, called SANGEET, that stores comprehensive information
of Hindustani Sangeet (North Indian Classical Music) compositions written by
famous musicologist Pt. Vishnu Narayan Bhatkhande. SANGEET preserves all the
required information of any given composition including metadata, structural,
notational, rhythmic, and melodic information in a standardized way for easy
and efficient storage and extraction of musical information. The dataset is
intended to provide the ground truth information for music information research
tasks, thereby supporting several data-driven analysis from a machine learning
perspective. We present the usefulness of the dataset by demonstrating its
application on music information retrieval using XQuery, visualization through
Omenad rendering system. Finally, we propose approaches to transform the
dataset for performing statistical and machine learning tasks for a better
understanding of Hindustani Sangeet. The dataset can be found at
this https URL

该论文提出了一种基于 XML 的公共数据集 SANGEET，其存储了印度北部古典音乐作品的全面信息，包括元数据、结构、符号、节奏和旋律信息，用于支持多个基于机器学习的音乐信息研究任务。

SANGEET: 一份基于 XML 的印度北方音乐研究开放数据集

SANGEET: A XML based Open Dataset for Research in Hindustani Sangeet

Visual sound source separation aims at identifying sound components from a
given sound mixture with the presence of visual cues. Prior works have
demonstrated impressive results, but with the expense of large multi-stage
architectures and complex data representations (e.g. optical flow
trajectories). In contrast, we study simple yet efficient models for visual
sound separation using only a single video frame. Furthermore, our models are
able to exploit the information of the sound source category in the separation
process. To this end, we propose two models where we assume that i) the
category labels are available at the training time, or ii) we know if the
training sample pairs are from the same or different category. The experiments
with the MUSIC dataset show that our model obtains comparable or better
performance compared to several recent baseline methods. The code is available
at
this https URL

本研究旨在通过视觉线索从给定声音混合物中识别声音组件。本研究提出了两个模型，分别使用单个视频帧，以音源类别作为分离过程的信息。在 MUSIC 数据集实验中，两个模型相比于几种基线方法获得了可比较或更好的性能。

利用分类信息进行单帧视觉声源分离

Leveraging Category Information for Single-Frame Visual Sound Source  Separation

We introduce PixelPlayer, a system that, by leveraging large amounts of
unlabeled videos, learns to locate image regions which produce sounds and
separate the input sounds into a set of components that represents the sound
from each pixel. Our approach capitalizes on the natural synchronization of the
visual and audio modalities to learn models that jointly parse sounds and
images, without requiring additional manual supervision. Experimental results
on a newly collected MUSIC dataset show that our proposed Mix-and-Separate
framework outperforms several baselines on source separation. Qualitative
results suggest our model learns to ground sounds in vision, enabling
applications such as independently adjusting the volume of sound sources.

PixelPlayer 是一种利用大量无标注视频进行学习的系统，该系统能够学习定位产生声音的图像区域，并将输入的声音分离成代表每个像素声音的组件。实验结果表明，我们提出的混合和分离框架在音源分离方面优于几种基线模型。