Detailed walkthrough on the complete process of audio source separation with concept, code and functioning app.
Audio source separation is a wonderful use case where every one of us can easily relate with and in this article we are going to deep dive into technical implementations of the solution where we will build an Android app with the TensorFlow model performing audio source separation.
This article will be the first in a two-part series where we will focus on model building activities in the first part, followed by a dedicated article explaining model deployment and data processing on the Android app.
Before getting into the concept, let me tell you we are going to create a faithful reproduction of the Official Spleeter solution in the TF2.0 leveraging its latest features, but for Android use. For those who are not aware, Spleeter is an industry standard audio source separation library and their solution performs at amazing accuracy in splitting the audio into different stems. So, kudos to the authors for building such a wonderful solution.
A Definitive Guide for Audio Processing in Android with TensorFlow Lite Models. Explanation with a demo app implementatio. This guide describes how to process audio files in Android, in order to feed them into deep learning models built using TensorFlow.
With the launch of TensorFlow Lite, embedding machine learning models in Android apps has never been easier. TensorFlow lite provides a
Audio Source Separation, also known as the Cocktail Party Problem, is one of the biggest problems in audio because of its practical use in so many situations. We propose a way to solve this problem using the advents of deep learning.
Sometimes, while doing programming, we need to go through some audio processing stuff. Some of the most used audio processing tasks in programming include — loading and saving audio files, splitting
Tensorflow Lite is one of my favourite software packages. It enables easy and fast deployment on a range of hardware and now comes with a wide range of delegates to accelerate inference — GPU, Core ML and Hexagon, to name a few. One drawback of Tensorflow Lite however is that it’s been designed with mobile applications in mind, and therefore isn’t optimised for Intel & AMD x86 processors. Better x86 support is on the Tensorflow Lite development roadmap, but for now Tensorflow Lite mostly relies on converting ARM Neon instructions to SSE via the Neon_2_SSE bridge