Audio Classification using Convolutional Neural Net

Demonstrates converting mobile audio recordings from m4a to wav for machine learning, using cloud convert and basic file organization, with optional Python automation.

Create a train and test folder split for audio clips, allocating about 90% to training and 10% to testing, while balancing across environments to evaluate the model.

Implement a Python driven train-test split by copying or moving negative and positive clips into train and test folders using a for loop and shuttle commands, with counters tracking totals.

Load and play audio files in Python by sorting train sorted files into negative (label zero) and positive (label one), then load with librosa and play using IPython display.

Explore four stages to convert audio clips into a spectrogram. Load audio with librosa, transform to frequency domain, apply amplitude to decibel, and expand to 3d tensor for cnn.

Process train sorted WAV clips through five stages: load audio with Librosa, apply Fourier transform, convert to decibel spectrogram, resize to a 3D tensor, and assemble into audio_clips for TensorFlow.

Learn to assemble a sequential CNN with convolutional and max pooling layers, flatten and dense layers, then compile with Adam and binary cross-entropy, and review the model summary.

Save the trained cnn as an h5 file, set the target directory for Raspberry Pi deployment, load the saved model, and run predictions to validate performance on the dataset.

Connect to the Raspberry Pi 5 via SSH, update and upgrade packages, enable the VNC server with raspi-config, and access the pi remotely with the VNC viewer, while monitoring temperature.

Record three-second audio clips on a Raspberry Pi 5 using a USB microphone, save them as WAV files in the project folder, and feed each clip to a convolutional neural net to determine whether it contains the word Ahmed.

Record a three-second audio clip with a USB mic on Raspberry Pi 5, preprocess with librosa, FFT, and decibel spectrogram, then predict with h5 model and drive hardware via GPIO.

Test a convolutional neural network-based audio classification model on Raspberry Pi 5, using a mic and servo to act when predictions exceed 0.9, even in noise.

Learn to prepare an audio dataset for convolutional neural nets by loading audio with Librosa, applying short-time Fourier transform, converting to decibel spectrograms, and expanding dimensions for neural network input.

the lecture demonstrates adapting a cnn-based audio classification workflow to an rnn with lstm using keras, detailing architecture changes, activation choices, and binary classification performance.