Build a Deep Audio Classifier with Python and Tensorflow

Introduction

Ever wondered how voice assistants like Siri or Alexa interpret your voice commands? In this article, we will walk you through building your deep audio classification model using Python and TensorFlow. By the end, you will understand how to process audio data, convert it into a numerical representation, and classify it using a convolutional neural network (CNN). We're in for quite a journey, so let’s get started!

Introduction to Audio Classification

In audio classification, the first step is to convert audio data into numerical representations. We will focus on using spectrograms, which allow us to apply computer vision techniques like CNNs for classification tasks. We will implement a sliding window classification approach to analyze longer audio clips, which is especially useful for tasks like speech command recognition.

For this tutorial, we will utilize audio data from the Z by HP Unlock Challenge, aiming to classify recordings of capuchin monkey calls.

Preparing the Environment

To start, you will need to download the appropriate dataset from Kaggle, which includes bird call clips of capuchins and other non-target audio. Once you have the data, set up the folder structure to include three separate directories for forest recordings, capuchin bird clips, and non-capuchin bird clips. All code used in this tutorial is available in the GitHub repository linked in the description.

You will install necessary dependencies, including TensorFlow, TensorFlow I/O for audio processing, and Matplotlib for visualization purposes.

!pip install tensorflow tensorflow-io matplotlib

Next, you will import the necessary libraries:

import os
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_io as tfio

Data Loading and Preprocessing

First, we define the paths to our target directories and create a data loading function that converts audio files into waveforms.

def load_wave_16k_mono(file_path):
    file_content = tf.io.read_file(file_path)
    waveform, sample_rate = tf.audio.decode_wav(file_content, desired_channels=1)
    return waveform, sample_rate

This function reads in the audio files, decodes them, and ensures they are in a mono format.

Next, we will prepare our dataset by loading positive (capuchin) and negative (non-capuchin) examples while labeling them accordingly. As our training data set might include an imbalance between classes, we must keep that in mind during modeling.

Creating Spectrograms

After loading the data, we need to create spectrograms using the Short-Time Fourier Transform (STFT). This step converts our waveforms into visual representations that can be processed by our CNN model.

def preprocess(file_path, label):
    waveform, _ = load_wave_16k_mono(file_path)
    spectrogram = tf.signal.stft(waveform, frame_length=320, frame_step=32)
    return tf.abs(spectrogram), label

The preprocessing function outputs the spectrogram along with the label for classification.

Building the Deep Learning Model

We will now construct a deep learning classification model using TensorFlow's Keras API.

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Flatten, Dense

model = Sequential([
    Conv2D(16, (3, 3), activation='relu', input_shape=(1491, 257, 1)),
    Conv2D(16, (3, 3), activation='relu'),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

In this model, we’re using two convolutional layers, a flattening layer, and a dense layer to make binary predictions. The model will be trained on spectrograms derived from our audio segments.

Training the Model

Using the prepared dataset, it is time to train the model. The training process will help the model learn the features distinguishing capuchin calls from other sounds.

history = model.fit(train_data, validation_data=validation_data, epochs=4)

After training, you can visualize the performance metrics like loss and accuracy to evaluate how well the model has learned to classify the audio clips.

Making Predictions

Once the model is trained, we can slide it over longer audio recordings, creating smaller windows to process and classify. The output will tell us the count of capuchin calls in each clip.

We will then aggregate the results into a CSV file for submission to the Z by HP unlock challenge.

CSV Export

To complete our project, we can export the final counts into a CSV file:

import csv

with open('results.csv', mode='w', newline='') as f:
    writer = csv.writer(f)
    writer.writerow(['Recording', 'Capuchin Calls'])
    for key, value in results.items():
        writer.writerow([key, value])

Conclusion

In this tutorial, we have built a deep audio classifier using Python and TensorFlow. We covered everything from data loading and preprocessing to training a deep learning model and making predictions on new data. With this knowledge, you can take on your audio classification challenges!

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Keywords

• Audio Classification
• TensorFlow
• Spectrogram
• Convolutional Neural Network (CNN)
• Machine Learning
• Deep Learning
• Data Preprocessing

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FAQ

Q1: What is an audio spectrogram?
A: An audio spectrogram is a visual representation of the spectrum of frequencies in an audio signal as they vary with time.

Q2: Why do we use convolutional neural networks for audio classification?
A: CNNs excel at identifying patterns in grid-like data, like images or spectrograms, making them effective for audio classification tasks.

Q3: Can I use this approach for other species' audio recognition?
A: Yes, the methodology can be adapted to classify audio clips of different species by changing the dataset and labels accordingly.

Q4: How can I improve the performance of my audio classification model?
A: You can improve model performance by using data augmentation techniques, balancing your datasets, or experimenting with hyperparameters.