Building an AI Musician in Max: Setup, MFCC Analysis, and More
Table of Contents:
- Introduction
- Building an AI Computer Improviser using Max MSP
- External Libraries: ml Star and flukoma
- Overview of Max MSP, Super Collider, and Pure Data
- Building the First Step: Analysis of a Two Piano Piece
- Creating the mfcc grains Patch
- Analysis using Mel Frequency Cepstral Coefficients (MFCC)
- Using Buffers for Data Storage
- Standardizing and Reducing Data Dimensions
- Normalizing the Data and Using UMAP for Dimension Reduction
- Querying the KD Tree for Nearest Data Points
- Formatting Data for Playback
- Highlighting Selected Data Points on the Plotter
- Creating Labels to Organize Data Using K-Means Clustering
- Conclusion
🎵 Building an AI Computer Improviser using Max MSP
In this multi-part Tutorial series, we will be exploring the process of building an AI computer improviser using Max MSP. Throughout this series, we will utilize external libraries such as ml Star and flukoma to enhance our improvisation capabilities. Max MSP is a versatile audio processing software that integrates machine learning algorithms and audio analysis techniques. We will also touch upon Super Collider and Pure Data, as these platforms provide similar functionalities.
🔬 Overview of Max MSP, Super Collider, and Pure Data
Max MSP is a visual programming language that allows users to create interactive audio and visual applications. Super Collider is an object-oriented programming language specifically designed for audio synthesis and algorithmic composition. Pure Data, similar to Max MSP, is an open-source visual programming language for multimedia and Music creation.
🎹 Building the First Step: Analysis of a Two Piano Piece
To begin our journey, we will build a patch in Max MSP for analyzing a two piano piece called "Distances". This patch will automatically trigger an analysis based on Mel Frequency Cepstral Coefficients (MFCC) and provide a means to Visualize and query the analyzed data. Additionally, we will utilize a sampler created in gen for playback purposes.
🔍 Analysis using Mel Frequency Cepstral Coefficients (MFCC)
Mel Frequency Cepstral Coefficients (MFCC) is a common technique used in audio signal processing for analyzing the spectral content of audio signals. In this step, we will delve into the implementation of MFCC analysis using the fluid.buff-mfcc object from the flukoma library. We will specify the number of coefficients, the source buffer, and the frame size.
💾 Using Buffers for Data Storage
In our analysis, we will be utilizing buffers for storing audio data and analysis results. We will explore how to create and manipulate buffers in Max MSP and how to efficiently retrieve data from them for further processing.
📊 Standardizing and Reducing Data Dimensions
After performing the MFCC analysis, we will standardize the data to ensure consistency and eliminate any biases. Following that, we will employ the UMAP (Uniform Manifold Approximation and Projection) algorithm to reduce the dimensionality of the data. This reduction is necessary to visualize the data effectively and identify Patterns and clusters.
⚖️ Normalizing the Data and Using UMAP for Dimension Reduction
To prepare our data for visualization, we will normalize the reduced two-dimensional data using the fluid.normalize object. This will Scale the data to a range of 0 to 1, allowing for better visualization on the plotter. We will then utilize UMAP to plot the normalized data in a two-dimensional space, visualizing the relationships between different data points.
🌐 Querying the KD Tree for Nearest Data Points
To explore the nearest data points to a selected data point, we will implement a KD (K-Dimensional) Tree using the fluid.kd-tree object. The KD tree allows for efficient searching and retrieval of the nearest neighbors to a given data point. We will utilize the K-Nearest algorithm to find the closest data points to the selected point on the plotter.
🎶 Formatting Data for Playback
In order to play back the selected data points, we need to format the data into a suitable format. We will calculate the start and end times based on the selected data point, and create a formatted message for playback. This formatted message will include parameters such as start time, end time, gain, speed, and window Shape.
🎛️ Highlighting Selected Data Points on the Plotter
To enhance the user experience, we will implement a feature that highlights the selected data points on the plotter. This will allow users to visually identify the data points they have chosen for playback. We will use a combination of Max MSP objects, including trigger, select, and cycle, to achieve this highlighting effect.
🔖 Creating Labels to Organize Data Using K-Means Clustering
In order to organize and categorize our data, we will implement K-Means clustering using the flucoma.k-means object. This algorithm groups similar data points together based on their features. We will create a label set using the resulting clusters, which will help us identify and distinguish different groups of data points on the plotter.
🔎 Conclusion
In this article, we have explored the process of building an AI computer improviser using Max MSP and external libraries. We have covered various steps, including audio analysis using MFCC, data storage using buffers, dimension reduction using UMAP, querying the KD tree for nearest data points, formatting data for playback, highlighting selected data points, and creating labels using K-Means clustering. With these techniques, we can create dynamic and interactive computer improvisation systems. Stay tuned for future articles in this series.
🎉 Highlights:
- Building an AI computer improviser using Max MSP
- Analysis using Mel Frequency Cepstral Coefficients (MFCC)
- Utilizing buffers for data storage
- Dimension reduction using UMAP
- Querying the KD tree for nearest data points
- Formatting data for playback
- Highlighting selected data points on the plotter
- Creating labels using K-Means clustering
【FAQ】
Q: What is Max MSP?
Max MSP is a visual programming language that allows users to create interactive audio and visual applications.
Q: What are Mel Frequency Cepstral Coefficients (MFCC)?
Mel Frequency Cepstral Coefficients (MFCC) is a technique used to extract features from audio signals for analysis and classification.
Q: What is UMAP?
UMAP (Uniform Manifold Approximation and Projection) is a dimension reduction technique used to visualize high-dimensional data in lower dimensions.
Q: How does K-Means clustering work?
K-Means clustering is an unsupervised machine learning algorithm used to group data points into distinct clusters based on their similarity.
Q: Can Max MSP be used for live performances?
Yes, Max MSP is commonly used for live performances, as it allows for real-time audio manipulation and interactivity.
【Resources】
