The Concatenator: A Bayesian Approach To Real Time Concatenative Musaicing (2411.04366v1)

Abstract: We present The Concatenator,'' a real time system for audio-guided concatenative synthesis. Similarly to Driedger et al.'smusaicing'' (or ``audio mosaicing'') technique, we concatenate a set number of windows within a corpus of audio to re-create the harmonic and percussive aspects of a target audio stream. Unlike Driedger's NMF-based technique, however, we instead use an explicitly Bayesian point of view, where corpus window indices are hidden states and the target audio stream is an observation. We use a particle filter to infer the best hidden corpus states in real-time. Our transition model includes a tunable parameter to control the time-continuity of corpus grains, and our observation model allows users to prioritize how quickly windows change to match the target. Because the computational complexity of the system is independent of the corpus size, our system scales to corpora that are hours long, which is an important feature in the age of vast audio data collections. Within The Concatenator module itself, composers can vary grain length, fit to target, and pitch shift in real time while reacting to the sounds they hear, enabling them to rapidly iterate ideas. To conclude our work, we evaluate our system with extensive quantitative tests of the effects of parameters, as well as a qualitative evaluation with artistic insights. Based on the quality of the results, we believe the real-time capability unlocks new avenues for musical expression and control, suitable for live performance and modular synthesis integration, which furthermore represents an essential breakthrough in concatenative synthesis technology.

• The paper introduces a Bayesian particle filtering approach for real-time concatenative synthesis that efficiently navigates large audio corpora.
• It demonstrates real-time synthesis with tunable parameters such as grain length and pitch shifting, empowering live creative applications.
• The approach scales independently of corpus size while ensuring reliable pitch reproduction and adaptable performance across diverse audio datasets.

The Concatenator: A Bayesian Approach to Real-Time Concatenative Musaicing

This paper introduces "The Concatenator," a novel system for real-time concatenative synthesis using a Bayesian framework. It represents a significant advancement in the field of audio mosaicing, building upon and diverging from previous non-negative matrix factorization (NMF) based approaches, such as those developed by Driedger et al. Unlike traditional musaicing techniques that rely heavily on pre-computed data, The Concatenator offers real-time synthesis capable of handling vast audio corpora.

The core concept relies on a Bayesian formulation where indices of audio corpus windows are treated as hidden states and a particle filter infers these states dynamically. This approach enables the system to maintain an efficient computational framework that remains independent of the corpus size, thus scaling well with extensive audio libraries. Such scalability is crucial given the increasing availability of digital audio data.

Technical Contributions

1. Bayesian Approach: The transition from NMF-based musaicing to a Bayesian model is the crux of this research. The methodology treats corpus window indices as hidden states, which allows for the real-time inference of the best match for the target audio stream using particle filters.
2. Real-Time Synthesis: Unlike systems that require pre-processing, The Concatenator performs the synthesis in real-time, facilitating immediate feedback and interaction for artists. The tunable parameters provided, such as grain length and pitch shifting, can be adjusted dynamically, offering musicians a novel interactive tool for live performance and experimentation.
3. Tunable Parameters: The system includes a parameter that adjusts the time-continuity of the audio grains and another that dictates the rapidity with which windows adjust to the target audio. These parameters give composers granular control over the synthesis, allowing for both precise and experimental use cases.
4. Computational Efficiency: The system's ability to operate independently of corpus size is a significant benefit. Based on computational complexity analysis, The Concatenator conducts numerous small-scale KL-based NMF problems online, leveraging random sampling to maintain an efficient operation in real time.

Evaluation and Implications

Through both quantitative and qualitative evaluations, the paper demonstrates that The Concatenator offers reliable pitch reproduction and faithful musical renditions in real-time. This performance was benchmarked on a variety of corpora, ranging from small datasets to extensive multi-hour libraries. The implications for music production and sound design are substantial; artists can now utilize large-scale sound datasets for creative synthesis without the need for lengthy pre-processing steps.

The qualitative application tests reveal that while the system might struggle with overly complex harmonic structures, it excels in recreating simpler melodies and rhythms with high fidelity. The stochastic element of particle filtering introduces a unique randomness which can be aesthetically desirable for some creative applications.

Future Directions

The paper suggests several future research directions. Enhancing the system's ability to handle complex harmonies and reducing the grain length variance are potential areas for improvement. Additionally, incorporating convolutional strategies over the current mel spectrogram approach or leveraging a streaming Constant-Q Transform might provide better low-frequency resolution and enhance the system’s versatility.

The Concatenator thus paves the way for an innovative real-time audio synthesis paradigm, enabling enhanced creative control and expanding the operational limits of concatenative sound synthesis. As exploration into the practical integration of such systems continues, further applications could emerge across varying domains such as live electronic performances, interactive multimedia installations, and advanced music production environments.