“Transforming the Music Industry with AI: An In-Depth Exploration of Deep Learning for Music Analysis and Categorization”

Source: Medium Article

Introduction

Music transcription and music classification are two important tasks in the field of music information retrieval (MIR). The former refers to the process of converting an audio recording of a musical performance into a symbolic representation, typically in the form of sheet music or a MIDI file. The latter refers to the process of categorizing music into various genres or other types of classes. Both tasks have traditionally been performed by humans, but with the advent of machine learning and artificial intelligence (AI), automated methods have become increasingly common.

In this article, we will explore how AI can be used to perform music transcription and music classification, using Python and a few popular libraries. We will begin by discussing music transcription.

Music Transcription

Music transcription involves the conversion of an audio recording of a musical performance into a symbolic representation, such as sheet music or a MIDI file. There are a variety of approaches to music transcription, ranging from rule-based systems to machine learning algorithms. In this article, we will focus on a machine learning approach, using a deep learning model to perform music transcription.

Data Preparation

To train a deep learning model for music transcription, we first need to prepare a dataset of audio recordings and their corresponding symbolic representations. There are several publicly available datasets that can be used for this purpose, such as the MAPS dataset or the MAESTRO dataset. In this article, we will use the MAPS dataset, which contains over 1000 audio recordings of classical piano music, along with their corresponding MIDI files.

Once we have our dataset, we need to extract features from the audio recordings that can be used as input to our deep learning model. One common approach is to use a spectrogram, which is a visual representation of the frequency content of an audio signal. We can compute a spectrogram using the Librosa library, which provides a number of audio processing functions.

import librosa

# Load audio file
audio, sr = librosa.load('audio_file.wav')

# Compute spectrogram
spectrogram = librosa.feature.melspectrogram(audio, sr=sr)

The result of this code is a 2D array representing the spectrogram of the audio file. You can visualize the spectrogram using the Matplotlib library:

import matplotlib.pyplot as plt
plt.imshow(spectrogram, aspect='auto', origin='lower', cmap='magma')
plt.xlabel('Time (frame)')
plt.ylabel('Frequency (Hz)')
plt.colorbar()
plt.show()

This will display a visualization of the spectrogram, with time on the x-axis and frequency on the y-axis.

Model Architecture

Once we have extracted features from our audio recordings, we can use them to train a deep learning model for music transcription. There are many different architectures that can be used for this task, but one popular choice is the convolutional recurrent neural network (CRNN).

CRNN combines a convolutional neural network (CNN) and a recurrent neural network (RNN), allowing it to capture both local and temporal features of the input. We can implement a CRNN using the Keras library, which provides a high-level interface for building deep learning models.

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, LSTM, Dense

model = Sequential()

# Add convolutional layers
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 647, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128,(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
# Flatten the output of the convolutional layers
model.add(Flatten())

# Add recurrent layers
model.add(LSTM(64, return_sequences=True))
model.add(LSTM(64))

# Add dense layers for classification
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

This model consists of several convolutional layers to extract features from the spectrogram, followed by recurrent layers to capture temporal dependencies, and finally dense layers for classification. The output of the model is a probability distribution over the different classes, which can be used to predict the class of a given audio recording.

Training and Evaluation

To train the CRNN model for music transcription, we can use the Keras API and TensorFlow backend. We first need to compile the model, specifying the loss function, optimizer, and metrics to use during training.

```python
model.compile(loss='categorical_crossentropy',
 optimizer='adam',
 metrics=['accuracy'])

We can then train the model on our dataset of audio recordings and their corresponding MIDI files.

## Training and Evaluation
model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=32)score = model.evaluate(X_test, y_test, batch_size=32)
print(‘Test loss:’, score[0])
print(‘Test accuracy:’, score[1])

Once the model is trained, we can evaluate its performance on a test set of audio recordings and their corresponding MIDI files.

score = model.evaluate(X_test, y_test, batch_size=32)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

Music Classification

Music classification involves the categorization of music into various genres or other types of classes. Like music transcription, there are a variety of approaches to music classification, ranging from rule-based systems to machine learning algorithms. In this article, we will focus on a machine learning approach, using a deep learning model to perform music classification.

Data Preparation

To train a deep learning model for music classification, we need a dataset of audio recordings and their corresponding labels. There are several publicly available datasets that can be used for this purpose, such as the GTZAN dataset or the FMA dataset. In this article, we will use the GTZAN dataset, which contains 1000 audio recordings of 10 different music genres.

Once we have our dataset, we need to extract features from the audio recordings that can be used as input to our deep learning model. One common approach is to use a mel spectrogram, which is a visual representation of the frequency content of an audio signal, with the frequencies scaled according to the mel scale.

We can compute a mel spectrogram using the Librosa library, which provides a number of audio processing functions.

import librosa

# Load audio file
audio, sr = librosa.load('audio_file.wav')

# Compute mel spectrogram
mel_spectrogram = librosa.feature.melspectrogram(audio, sr=sr)

The result of this code is a 2D array representing the mel spectrogram of the audio file. We can visualize the mel spectrogram using the Matplotlib library:

import matplotlib.pyplot as plt
plt.imshow(mel_spectrogram, aspect='auto', origin='lower', cmap='magma')
plt.xlabel('Time (frame)')
plt.ylabel('Mel frequency')
plt.colorbar()
plt.show()

This will display a visualization of the mel spectrogram, with time on the x-axis and mel frequency on the y-axis.

Model Architecture

Once we have extracted features from our audio recordings, we can use them to train a deep learning model for music classification. There are many different architectures that can be used for this task, but one popular approach is to use a convolutional neural network (CNN) followed by one or more dense layers.

CNN is used to extract features from the mel spectrogram, while dense layers are used to perform classification based on the extracted features. The output of the model is a probability distribution over the different music genres, which can be used to predict the genre of a given audio recording.

Here’s an example of a simple CNN-based model for music classification using Keras:

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

num_classes = 10

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 647, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax')

This model consists of two convolutional layers with max pooling, followed by a dense layer with ReLU activation and a final dense layer with softmax activation for classification.

Training and Evaluation

To train the music classification model, we can use the Keras API and the TensorFlow backend. We first need to compile the model, specifying the loss function, optimizer, and metrics to use during training.

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

We can then train the model on our dataset of audio recordings and their corresponding labels.

model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=10, batch_size=32)

Once the model is trained, we can evaluate its performance on a test set of audio recordings and their corresponding labels.

score = model.evaluate(X_test, y_test, batch_size=32)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

Conclusion

In this article, we have explored two applications of AI in music: music transcription and music classification. We have discussed the data preparation, model architecture, training, and evaluation steps for each of these tasks, using examples from the field of deep learning.

While these approaches are not without their limitations, they have the potential to greatly improve the efficiency and accuracy of many tasks in music, from composition and performance to analysis and understanding. As AI technology continues to develop, we can expect to see even more sophisticated and powerful applications of AI in the field of music.