Audio Classification Using Convolutional Neural Net

Audio Classification using Convolutional Neural Net with Raspberry 5 AI Model Deployment

What you'll learn

Define the real Audio Environments to record clips for Machine Learning Model Prediction.

Compose Negative and Positive Audio Clips for Machine Learning use.

Inject “Audio Keyword” to trigger RASPI 5 processor action in Positive Audio Clips.

Slice an Audio file into several Audio Clips for Neural Net feeding.

Apply the 5 Stages of Raw Audio Preparation for Neural Net use (load, time domain, frequency domain, spectrogram, and resize).

Use Librosa, Spectrogram, and Decibel of raw Audio for Neural Net use.

Apply Labeling Audio Clips as Positive Audio and Negative Audio for the use of Neural Net Training.

Slicing, Labeling, and Batching Audio Clips for Neural Net use.

Use Google Colab to create a Python Program with Convolutional Neural networks for Audio Classification and Prediction to be saved as an H5 AI Model.

Assemble the Raspberry Pi 5 and other Hardware Devices for Audio Prediction use.

Install Raspberry Pi 5 Software Requirements, including the Operating System, VNC viewer, Librosa, Tensorflow, and more.

Use FileZilla to transfer the H5 AI Model to be saved in Raspberry Pi 5.

Deploy and Run the H5 AI Model inside the Raspberry Pi 5 and make the Audio Prediction.

Use the Raspberry Pi 5 Audio Prediction to control the movement of the Servo Motor.

Requirements

Basic knowledge of Python and basic knowledge of AI.

Description

This course is designed to provide a real understanding of handling audio files in machine learning. This course will give you a complete track record of processing audio files from A to Z using Python. This course will explain how to use Convolutional Neural Networks to generate an H5 AI model for audio classification purposes. This course gives you a complete understanding of Raspberry Pi 5 assembly, programming, AI Model deployment, and prediction of audio files. We will learn how to identify audio environments for machine-learning purposes. We will learn how to record audio files and slice them into clips of positive and negative types. How to process the raw audio clips and inject the “keyword” to be detected by the neural network. Apply clip labeling, clip slicing, and clip batching for the preparation of feeding audio clips to the Neural Net.  Apply the required stages (load, time domain, frequency domain, spectrogram, and resize) to process raw audio clips for prediction use. Use Python programming to generate an H5 AI model for audio prediction purposes. Deploy and run the H5 AI model inside the Raspberry Pi 5 to control the movement of the servo motor with audio order.  Testing the model with a real-time audio prediction process.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Preparing Audio Files for Machine Learning use.

Lecture 2 Define Audio Environments and Audio Keyword

Lecture 3 Recording First Environment (Silent)

Lecture 4 Recording Second Environment (Family Talk)

Lecture 5 Recording Third Environment (Family Talk + Glass Down)

Lecture 6 Audio Convert to WAV

Lecture 7 Compose Positive Audio

Lecture 8 Compose Positive Audio - Family Talk Environment

Lecture 9 Slicing Single Audio File into 3 Seconds Multiple Clips in Python

Lecture 10 Slicing Audio, Balancing Positive and Negative

Lecture 11 Train-Test Audio Dataset Split using Copy-Paste

Lecture 12 Train-Test Audio Dataset Split using Python - Part 1

Lecture 13 Train-Test Audio Dataset Split using Python - Part 2

Section 3: Handling Audio Files in Python.

Lecture 14 Sorting Audio Clips

Lecture 15 Load and Play Audio Files in Python

Lecture 16 Spectrogram

Lecture 17 Five Stages Audio Processing

Section 4: Audio Labeling, Audio Pipeline, Audio Slicing, and Audio Batching

Lecture 18 Labeling Basics and Meaning

Lecture 19 Labeling Audio Clips as Positive and Negative

Lecture 20 Batching

Section 5: Convolutional Neural Networks CNN for Audio Classification

Lecture 21 CNN Libraries, Layers, Compile and Summary

Lecture 22 CNN train-test Audio Files Split and Model Fit

Lecture 23 CNN Make a Prediction on Audio Files

Lecture 24 CNN Save as H5 and Test the Model

Section 6: Assembly of Raspberry Pi 5 and installing Software and Python Libraries

Lecture 25 Raspberry Pi 5 and other Hardware Devices

Lecture 26 Assembly of Raspberry Pi 5 (Thermal Pads, Active Cooler, and Power Supply)

Lecture 27 Raspberry Pi 5 Operating System OS Installation

Lecture 28 Raspberry Pi 5 Connection and VNC Installation

Section 7: Deploy and Run AI Audio Model in Raspberry Pi 5

Lecture 29 Raspberry Pi 5 Virtual Environment and Install of TensorFlow

Lecture 30 Raspberry Pi 5 Librosa Install and AI Model Copy with FileZilla

Lecture 31 Raspberry Pi 5 Audio Recording

Lecture 32 Raspberry Pi 5 Audio Prediction

Lecture 33 Raspberry Pi 5 and Servo Motor Connection

Lecture 34 Run and Test AI Audio Model in Raspberry Pi 5

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