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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Source Separation

By: Quantiphi Latest Version: 1.0

Product Overview

An end-to-end source separation in the time-domain. Vocal separation of an input source audio. It uses a convolutional neural network to separate the background music/noise and vocals in an input wav/mp3 audio file. The model package is an adaptation of the U-Net architecture to the one-dimensional time domain. The prediction is in the form of two audio files comprising of the vocals and accompaniment (background noise/ music). The output is a zipped folder containing the above two audio files.

Key Data

Version
By Quantiphi
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Support Information

AWS Infrastructure

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Refund Policy

This product is offered for free. If there are any questions, please contact us for further clarifications.

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