Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models (2411.14972v1)

Abstract: This paper introduces Open-Amp, a synthetic data framework for generating large-scale and diverse audio effects data. Audio effects are relevant to many musical audio processing and Music Information Retrieval (MIR) tasks, such as modelling of analog audio effects, automatic mixing, tone matching and transcription. Existing audio effects datasets are limited in scope, usually including relatively few audio effects processors and a limited amount of input audio signals. Our proposed framework overcomes these issues, by crowdsourcing neural network emulations of guitar amplifiers and effects, created by users of open-source audio effects emulation software. This allows users of Open-Amp complete control over the input signals to be processed by the effects models, as well as providing high-quality emulations of hundreds of devices. Open-Amp can render audio online during training, allowing great flexibility in data augmentation. Our experiments show that using Open-Amp to train a guitar effects encoder achieves new state-of-the-art results on multiple guitar effects classification tasks. Furthermore, we train a one-to-many guitar effects model using Open-Amp, and use it to emulate unseen analog effects via manipulation of its learned latent space, indicating transferability to analog guitar effects data.

• The paper introduces a crowdsourced synthetic data framework that enhances audio effects modeling via neural network emulations.
• It employs a flexible augmentation strategy allowing any input audio and achieves state-of-the-art performance in guitar effects classification.
• The framework demonstrates promising transferability by generalizing to unseen analog devices through one-to-many effects modeling.

Overview of Open-Amp: A Synthetic Data Framework for Audio Effects Modeling

The paper "Open-Amp: Synthetic Data Framework for Audio Effect Foundation Models" introduces a novel approach to generating large-scale and diverse datasets for audio effects modeling. This work addresses the limitations inherent in traditional audio datasets, which are often constrained by scope and diversity of effects and input signals. The introduction of Open-Amp leverages user-generated neural network emulations of guitar amplifiers and effects to provide a flexible and highly customizable framework for data augmentation.

Objectives and Motivation

Digital audio effects modeling seeks to emulate analog processing devices, enabling the digitization of musical equipment. Traditional approaches often suffer from limited accessibility and variability, constrained by the hardware capabilities and the finite datasets of current audio effects processors. Open-Amp emerges from the necessity to overcome these limitations, by utilizing crowdsourced emulations that are accessible and cover a broader range of effects devices.

Key Contributions

Open-Amp delivers several significant contributions to the domain of audio effects processing:

1. Crowdsourced Dataset Generation: Utilizing open-source neural network modeling tools like GuitarML and Neural Amp Modeler, Open-Amp aggregates emulations of a variety of audio effects, significantly enhancing the diversity of training datasets.
2. Flexible Augmentation Capabilities: Unlike existing datasets tied to specific input signals, Open-Amp provides users with the capacity to select any input audio, ensuring broader applicability across various signals. During training, models can render audio online, affording dynamic and flexible augmentation.
3. State-of-the-Art Classification Performance: The framework has been employed to train a contrastive learning-based guitar effects encoder, achieving new state-of-the-art results in multiple effects classification tasks. The encoder demonstrated superior transferability compared to results on benchmark datasets, such as GFX and EGFX.
4. One-to-Many Effects Modeling: Open-Amp was used to create a foundational one-to-many guitar effects model capable of accommodating unseen analog effects through latent space manipulation, indicating strong potential for transferability to new data.

Experimental Insights

The authors provide comprehensive empirical evidence demonstrating the efficacy of Open-Amp in various tasks. For instance, in guitar effects classification, embeddings generated using Open-Amp significantly outperformed baseline models in transfer learning settings. A one-to-many model was also effectively trained on a large spectrum of synthetic data, with the ability to generalize across different effects, as shown by encoder embeddings outperforming state-of-the-art solutions in classification accuracy across multiple datasets.

Further experimentation showed the potential of Open-Amp for direct transfer learning to unseen analog devices. Fine-tuning a one-to-many model on limited data from novel devices achieved competitive results with dedicated one-to-one models, underlining the utility of Open-Amp's learned embeddings.

Implications and Future Directions

The introduction of Open-Amp marks a substantial advancement for the development of audio effect models. By providing a flexible and comprehensive synthetic dataset framework, it enables broader exploration and understanding of machine learning applications in music information retrieval and digital signal processing. Practically, it offers a more dynamic environment for simulating complex effects and ameliorates the data scarcity barrier typically encountered in training high-performance models.

The potential for future developments using Open-Amp includes expanding the dataset with additional effects and incorporating more complex signal processing architectures. Open-Amp could also integrate real-time augmentation in machine learning pipelines by extending its compatibility across varied sample rates and incorporating more detailed circuit models, thus broadening its utility and applicability within both academic research and industry practice. The groundwork laid by this framework hints at promising pathways to more generalized and adaptable audio effect modeling.