Overview / Usage

Join us in the thrilling adventure of "Unleash the Melodic Magic," where we bring music and artificial intelligence together to create an extraordinary experience that transcends imagination!

Unleash the Melodic Magic is an innovative and cutting-edge project that explores the intersection of music and AI technology. Our goal is to harness the power of OneDNN (OneAPI Deep Neural Network Library) to develop an advanced AI music generator that can compose original, captivating melodies with a touch of magical allure.

Why OneDNN?

OneDNN is at the forefront of deep learning acceleration and optimization, providing us with the tools and framework necessary to build a high-performance AI music generator. By leveraging the capabilities of OneDNN, we can ensure that the music generation process is efficient, seamless, and capable of meeting the demands of modern music enthusiasts.

About the Project:

The AI music generation project aims to tackle the challenge of creating original music compositions using deep learning techniques. By training LSTM neural networks on a dataset of MIDI files, the project enables the generation of unique and personalized soundtracks that can be used in various production scenarios.

One of the key problems addressed by this project is the time-consuming and often subjective process of composing music from scratch. Traditional composition methods require significant expertise and manual effort from composers, limiting the speed and diversity of music production. Moreover, finding the right balance between creativity and meeting specific requirements can be challenging. The AI music generator offers a solution by automating the composition process and providing a tool for quick and efficient music generation.

The research and work behind this project have resulted in a powerful AI model that can analyze and learn intricate patterns and structures within MIDI files. By understanding the relationships between musical elements like notes, durations, velocities, and more, the model can generate coherent and compelling musical sequences. This enables composers, content creators, and production teams to experience a new level of efficiency, creativity, and flexibility in their work.

In practical production settings, the AI music generator finds application in various creative industries. For example, in the gaming industry, where immersive soundtracks play a crucial role in enhancing the player experience, the AI-generated music can provide dynamic and adaptive compositions that respond to in-game events and actions. This adds depth and engagement to the gameplay, creating a more immersive and enjoyable environment.

Similarly, in the film and advertising industries, where music sets the tone and evokes emotions, the AI music generator offers a vast repertoire of original compositions. Content creators can easily generate customized soundtracks that align with their desired moods, atmospheres, and storytelling elements. This streamlines the production process and reduces the dependency on external composers, empowering creative teams to iterate and experiment with different musical styles and motifs.

Overall, the AI music generation project addresses the challenges of traditional music composition by leveraging deep learning techniques. By automating the composition process and providing a tool for efficient music generation, the project enables composers, content creators, and production teams to experience a new level of creativity, flexibility, and speed in their work. From gaming to film and advertising, the practical applications of AI-generated music are far-reaching, enhancing the overall audio experience and delivering unique soundtracks tailored to specific production needs.

Methodology / Approach

1. Data Preparation: The initial step involves obtaining a dataset of MIDI files containing musical compositions. The dataset is then preprocessed to extract relevant information such as notes, durations, velocities, and other musical attributes.
2. **Parsing MIDI Files: **MIDI files are parsed to extract the musical elements, such as notes and their attributes, from the tracks within the MIDI files. The parsed information is processed and stored in a suitable format for training the LSTM model.
3. Feature Engineering: Additional functions are implemented to process and transform the extracted musical elements into suitable features for training the model. This may include tasks such as converting notes to numerical representations, handling pauses between notes, and organizing the data into input-output pairs.
4. Model Architecture: The LSTM model architecture is defined, specifying the number of LSTM units, dropout rates, activation functions, and other hyperparameters. The model architecture is designed to capture the sequential nature of music and generate coherent musical sequences.
5. Model Training and Evaluation: The LSTM model is trained on the prepared input-output data pairs. The training process involves optimizing the model parameters using an appropriate loss function and an optimizer. The model's performance is evaluated using validation data to ensure that it generalizes well and produces meaningful musical sequences.
6. **Music Generation: **Once the model is trained, it can be used to generate new music. The trained model takes a seed sequence as input and predicts the next notes based on learned patterns. The process is repeated iteratively to generate longer musical sequences. Additional considerations such as randomness and temperature adjustments can be applied to control the diversity and creativity of the generated music.
7. **MIDI File Generation: **The generated musical sequences are converted into MIDI format to facilitate playback and further exploration. The MIDI files can be saved and played using appropriate software or libraries.

By following this methodology, we can create an AI music generator that is capable of learning from existing musical compositions and generating new musical sequences in a similar style. The process involves data preparation, model training, parsing MIDI files, feature engineering, model architecture design, model training and evaluation, music generation, and MIDI file generation.

Technologies Used

• Pandas and Numpy for data manipulation
• Mido for handling MIDI files
• IPython for audio playback
• Matplotlib and Librosa for visualizing audio data.
• Keras to construct and train our LSTM model.

Repository

https://github.com/SUBASHPALVEL/Unleash-the-Melodic-Magic

Collaborators