Perceptually Guided Audio Texture Generation using an Example-Based Framework

Core Concepts

An example-based framework to determine guidance vectors in the latent space of an unconditionally trained StyleGAN for controllable generation of audio textures based on user-defined semantic attributes.

Abstract

The paper proposes an example-based framework (EBF) to perceptually guide the generation of audio textures based on user-defined semantic attributes. The key highlights are:

The framework leverages the semantically disentangled latent space of an unconditionally trained StyleGAN to find guidance vectors for controlling attributes like "Brightness", "Rate", "Impact Type", and "Fill-Level" during texture generation.

It uses a few synthetic examples generated using a Gaver sound synthesizer to indicate the presence or absence of a semantic attribute. These examples are encoded into the latent space of the StyleGAN to infer the guidance vectors.

The effectiveness of the framework is validated through attribute rescoring analysis and perceptual listening tests. The results show that the framework can find user-defined and perceptually relevant guidance vectors for controllable generation of audio textures.

The framework is also demonstrated for the task of selective semantic attribute transfer between textures.

Stats

The number of impact sounds in a sample can be increased or decreased.
The brightness of an impact sound can be increased or decreased.
The type of impact sound (sharp vs. scraping) can be changed.
The fill level of a water-filling texture can be continuously varied.

Quotes

"To control the generation of audio textures, we analyze the disentangled latent space of a StyleGAN, to find guidance vectors based on user-defined semantic attributes."
"We define semantic attributes in audio as a set of factors that matter to human perception of sound."
"We generate synthetic sound examples representative of the semantic attribute we want to control during generation."

Key Insights Distilled From

Example-Based Framework for Perceptually Guided Audio Texture Generation

by Purnima Kama... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2308.11859.pdf

Deeper Inquiries

How can the framework be extended to handle out-of-distribution sounds, such as those generated by users through vocal queries, for navigating the latent space of the GAN

To extend the framework to handle out-of-distribution sounds, such as those generated by users through vocal queries, for navigating the latent space of the GAN, several adjustments and enhancements can be made:

Encoder Training: Modify the Encoder to be more robust in projecting out-of-distribution sounds into the latent space. This may involve training the Encoder on a diverse set of synthetic sounds that cover a wide range of variations and characteristics to improve its ability to map different types of sounds to the latent space accurately.

Querying Mechanisms: Develop new querying mechanisms that can interpret vocal queries and convert them into representations that can be used to navigate the latent space. This may involve integrating speech recognition technology to convert vocal inputs into text or numerical representations that can be processed by the framework.

Non-linear Traversal Methods: Implement non-linear traversal methods that can guide the generation process in a more adaptive and dynamic manner. By incorporating techniques that allow for non-linear edits in the latent space, the framework can better handle the complexities of out-of-distribution sounds and provide more accurate and meaningful results.

User Interface: Design a user-friendly interface that allows users to input vocal queries and interact with the framework effectively. Providing clear instructions and feedback mechanisms can enhance the user experience and facilitate the navigation of the latent space using vocal inputs.

By incorporating these enhancements, the framework can be adapted to handle out-of-distribution sounds generated by users through vocal queries, enabling more versatile and user-friendly interactions with the GAN for audio generation.

How can the framework be adapted to work with text-to-audio models, which rely on text captions for controllability, in a systematic manner

To adapt the framework to work with text-to-audio models in a systematic manner, the following steps can be taken:

Data Integration: Incorporate text-to-audio models into the framework by integrating them with the existing architecture. This may involve developing interfaces that allow seamless communication between the text-to-audio models and the framework for attribute controllability.

Attribute Mapping: Establish a mapping between the semantic attributes identified by the text-to-audio models and the attributes defined in the framework for audio texture generation. Ensuring consistency and alignment between the attributes identified by the text-to-audio models and the attributes used for controllability in the framework is essential for systematic operation.

Training and Fine-tuning: Train and fine-tune the text-to-audio models on datasets that align with the audio textures and attributes of interest in the framework. This process helps the models learn to generate audio outputs based on textual inputs that correspond to the desired semantic attributes for controllability.

Integration Testing: Conduct thorough integration testing to validate the compatibility and functionality of the text-to-audio models within the framework. This involves testing the models' ability to generate audio outputs based on textual inputs that reflect the desired attribute changes in the audio textures.

By following these systematic steps, the framework can be effectively integrated with text-to-audio models, enabling a more comprehensive and structured approach to attribute controllability in audio generation.

Can the framework be applied to other sound types beyond audio textures, such as musical instrument timbres or environmental sounds, by developing appropriate parametric sound synthesis models

To apply the framework to other sound types beyond audio textures, such as musical instrument timbres or environmental sounds, by developing appropriate parametric sound synthesis models, the following strategies can be implemented:

Parametric Synthesis Models: Develop parametric synthesis models tailored to the characteristics and attributes of the specific sound types of interest, such as musical instrument timbres or environmental sounds. These models should capture the unique features and variations present in the sound types to enable accurate representation and synthesis.

Semantic Attribute Definition: Define the semantic attributes relevant to the new sound types, considering factors that are perceptually significant for musical instrument timbres or environmental sounds. This step is crucial for identifying the key parameters that can be controlled and manipulated in the generation process.

Training Data Collection: Curate and collect training datasets that encompass a diverse range of musical instrument timbres or environmental sounds, annotated with the defined semantic attributes. The quality and diversity of the training data play a vital role in the effectiveness of the parametric sound synthesis models.

Attribute Guidance Vectors: Derive attribute guidance vectors specific to the new sound types by utilizing synthetic sound examples that represent the desired semantic attributes. These vectors should enable precise control and manipulation of the identified attributes during the generation process.

Evaluation and Validation: Conduct thorough evaluation and validation of the framework's performance on the new sound types, assessing its ability to accurately generate and control musical instrument timbres or environmental sounds based on the defined semantic attributes. This process helps ensure the effectiveness and applicability of the framework to diverse sound types.

By following these steps and customizing the framework to accommodate the unique characteristics of musical instrument timbres or environmental sounds, it can be successfully applied to a broader range of sound types beyond audio textures, enhancing its versatility and utility in sound generation applications.