音乐源分离:指南

Rudranil Das, Deepti Deshwal, P. Sangwan, Neelam Nehra
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引用次数: 0

摘要

随着印度电信业的革命和YouTube Shorts、Instagram reels等应用程序以及MX Takatak和Josh等本土应用程序的用户基础呈指数级增长,音乐质量也需要叛乱。一段迷人而活泼的背景音乐构成了上述所有应用程序的基础,而这些应用程序大多数时候都不容易获得。因此,音乐源分离(MSS)被证明是时效性的需要。MSS旨在分离音乐的各种组成部分,尽量减少它们之间的重叠。这些组成部分包括人声、低音、鼓和其他伴奏。鸡尾酒会效应是MSS的最佳例证。可以采用基于时域和基于谱图的方法来消除MSS问题。本研究的目的是观察和比较各种现有的基于深度学习的算法(基于时域的),如Conv Tasnet、Demucs和Open-Un-Mix。此外,我们已经实现了一个非常著名的卷积架构Demucs,并能够在MUSDB18数据集上实现7.2的SDR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Music Source Separation: A Guide
With the revolution in India's telecom industry and exponentially rising user base of applications like YouTube Shorts, Instagram reels, etc. and other indigenous apps like MX Takatak and Josh, insurgence in the quality of music is also needed. A captivating and a snappy background music piece form the foundation of all of the aforementioned apps, which most of the time is not readily available. Therefore, Music Source Separation (MSS) proves to be the need of the hour. MSS aims at segregating various constituting components of music with minimum possible overlap between them. These components (stems) include vocals, bass, drums and other accompaniments. Cocktail party effect illustrates MSS in the best way. The MSS problem can be eliminated using time domain based and spectrogram based methods. The purpose of this research is to look and compare the various existing deep learning-based algorithms (time domain based), such as Conv Tasnet, Demucs, and Open-Un-Mix. Also, we have implemented a very well-known convolutional architecture, Demucs, and were able to achieve the SDR of 7.2 evaluated on the MUSDB18 dataset.
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