Jamison  Fisher

Jamison Fisher

1619427720

CNNs for Audio Classification

A primer in deep learning for audio classification using tensorflow

Convolutional Neural Nets

CNNs or convolutional neural nets are a type of deep learning algorithm that does really well at learning images.

That’s because they can learn patterns that are translation invariant and have _spatial hierarchies _(F. Chollet, 2018).

Image by Author

That means if If the CNN learns the dog in the left corner of the image above, then it can identify the dog in the other two pictures that have been moved around (translation invariance).

If the CNN learns the dog from the left corner of the image above, it will recognize pieces of the original image in the other two pictures because it has learned what the edges of the her eye with heterochromia looks like, her wolf-like snout and the shape of her stylish headphones (spatial hierarchies).

These properties make CNNs formidable learners for images because the real world doesn’t always look exactly like the training data.

Can I use this for audio?

**Yes. **You can extract features which look like images and shape them in a way in order to feed them into a CNN.

This article explains how to train a CNN to classify species based on audio information.

#tensorflow #audio-classification #machine-learning #deep-learning #cnn

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CNNs for Audio Classification
Jamison  Fisher

Jamison Fisher

1619427720

CNNs for Audio Classification

A primer in deep learning for audio classification using tensorflow

Convolutional Neural Nets

CNNs or convolutional neural nets are a type of deep learning algorithm that does really well at learning images.

That’s because they can learn patterns that are translation invariant and have _spatial hierarchies _(F. Chollet, 2018).

Image by Author

That means if If the CNN learns the dog in the left corner of the image above, then it can identify the dog in the other two pictures that have been moved around (translation invariance).

If the CNN learns the dog from the left corner of the image above, it will recognize pieces of the original image in the other two pictures because it has learned what the edges of the her eye with heterochromia looks like, her wolf-like snout and the shape of her stylish headphones (spatial hierarchies).

These properties make CNNs formidable learners for images because the real world doesn’t always look exactly like the training data.

Can I use this for audio?

**Yes. **You can extract features which look like images and shape them in a way in order to feed them into a CNN.

This article explains how to train a CNN to classify species based on audio information.

#tensorflow #audio-classification #machine-learning #deep-learning #cnn

Brionna  Bailey

Brionna Bailey

1589788849

High Performance Web Audio with AudioWorklet in Firefox

AudioWorklet was first introduced to the web in 2018. Ever since, Mozilla has been investigating how to deliver a “no-compromises” implementation of this feature in the WebAudio API. This week, Audio Worklets landed in the release of Firefox 76. We’re ready to start bridging the gap between what can be done with audio in native applications and what is available on the web.

Now developers can leverage AudioWorklet to write arbitrary audio processing code, enabling the creation of web apps that weren’t possible before. This exciting new functionality raises the bar for emerging web experiences like 3D games, VR, and music production.

#audio #featured article #firefox #firefox releases #web apis #audio worklets #audioworklet #web audio api #webaudio

Gerhard  Brink

Gerhard Brink

1622533383

An Introduction to 4 Types of Audio Classification

Audio classification is the process of listening to and analyzing audio recordings. Also known as sound classification, this process is at the heart of a variety of modern AI technology including virtual assistants, automatic speech recognition, and text-to-speech applications. You can also find it in predictive maintenance, smart home security systems, and multimedia indexing and retrieval.

Audio classification projects like those mentioned above start with annotated audio data. Machines require this data to learn how to hear and what to listen for. Using this data, they develop the ability to differentiate between sounds to complete specific tasks. The annotation process often involves classifying audio files based on project-specific needs through the help of dedicated audio classification services.

In this article, we look at four types of classification and related use-cases for each.

#machine-learning #audio-content #data-classification #data-analysis #data-quality #data-labelling #how-to-label-data #nlp

Chaz  Homenick

Chaz Homenick

1594782660

Audio Classification in an Android App with TensorFlow Lite

Deploying machine learning-based Android apps is gaining prominence and momentum with frameworks like TensorFlow Lite, and there are quite a few articles that describe how to develop mobile apps for computer vision tasks like text classification and image classification.

But there’s very much less that exists about working with audio-based ML tasks in mobile apps, and this blog is meant to address that gap — specifically, I’ll describe the steps and code required to perform audio classification in Android apps.

Image for post
Tensorflow Lite Model on Android to make audio classification

Intended Audience and Pre-requisites:

This article covers different technologies required to develop ML apps in mobile and deals with audio processing techniques. As such, the following are the pre-requisite to get the complete understanding of the article:

→ Familiarity with deep learning, Keras, and convolutional neural networks

→ Experience with Python and Jupyter Notebooks

→ Basic understanding of audio processing and vocal classification concepts

→ Basics of Android app development with Kotlin

Note: If you’re new to audio processing concepts and would like to understand what MFCC [‘Mel Frequency Cepstral Coefficient’] is — pls refer this other blog of mine, where I have explained some of these concepts in detail.

I’ve provided detailed info with regard to various steps and processing involved, and have commented on the code extensively in GitHub for easier understanding. Still, if you have any queries, please feel free to post them as comments.

A Major Challenge

One major challenge with regard to development of audio-based ML apps in Android is the lack of libraries in Java that perform audio processing.

I was surprised to find that there are no libraries available in Java for Android that help with the calculation of MFCC and other features required for audio classification. Most of my time with regard to this article has been spent towards developing a Java components that generates MFCC values just like Librosa does — which is very critical to a model’s ability to make predictions.

What We’ll Build

At the end of the tutorial, you’ll have developed an Android app that helps you classify audio files present in your mobile sdcard directory into any one of the noise type of the Urbancode Challenge dataset. Your app should more or less look like below:

#tensorflow #heartbeat #tensorflow-lite #audio-classification #android #android app

Xander  Hane

Xander Hane

1618539300

Audio Classification with PyTorch’s Ecosystem Tools

Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification. In this blog post we will demonstrate such an example by using the popular method of converting the audio signal into the frequency domain.
This blog post is a third of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML/DL project. The previous blog posts focused on image classification and hyperparameters optimization. In this blog post, we will show how using Torchaudio and Allegro Trains enables simple and efficient audio classification.

#deep-learning-codebase #data-science #deep-learning #machine-learning #audio-classification