SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model (2410.02144v2)

Abstract: We present SoundMorpher, an open-world sound morphing method designed to generate perceptually uniform morphing trajectories. Traditional sound morphing techniques typically assume a linear relationship between the morphing factor and sound perception, achieving smooth transitions by linearly interpolating the semantic features of source and target sounds while gradually adjusting the morphing factor. However, these methods oversimplify the complexities of sound perception, resulting in limitations in morphing quality. In contrast, SoundMorpher explores an explicit relationship between the morphing factor and the perception of morphed sounds, leveraging log Mel-spectrogram features. This approach further refines the morphing sequence by ensuring a constant target perceptual difference for each transition and determining the corresponding morphing factors using binary search. To address the lack of a formal quantitative evaluation framework for sound morphing, we propose a set of metrics based on three established objective criteria. These metrics enable comprehensive assessment of morphed results and facilitate direct comparisons between methods, fostering advancements in sound morphing research. Extensive experiments demonstrate the effectiveness and versatility of SoundMorpher in real-world scenarios, showcasing its potential in applications such as creative music composition, film post-production, and interactive audio technologies. Our demonstration and codes are available at~\url{https://xinleiniu.github.io/SoundMorpher-demo/}.

Insightful Overview of "SoundMorpher: Perceptually-Uniform Sound Morphing with Diffusion Model"

The paper presents "SoundMorpher," an innovative method for sound morphing that leverages a diffusion model to achieve perceptually uniform transformations. Unlike traditional techniques, which often assume a linear relationship between morph factors and sound perception, SoundMorpher models the perceptual transitions explicitly by utilizing Mel-spectrogram features. This approach rectifies oversimplified assumptions and offers smoother, more consistent audio transformations.

Key Contributions

SoundMorpher introduces several important contributions:

1. Pre-trained Diffusion Model Utilization: It leverages a pre-trained diffusion model to perform sound morphing tasks without extensive retraining, enhancing its applicability to various real-world tasks.
2. Sound Perceptual Distance Proportion (SPDP): This new metric quantitatively links morph factors and perceptual stimuli, enabling more seamless perceptual transitions.
3. Quantitative Evaluation Metrics: SoundMorpher adapts objective metrics to evaluate correspondence, perceptual intermediateness, and smoothness, addressing a lack of quantitative evaluation in sound morphing methodologies.
4. Versatile Real-world Applications: Demonstrates effective application in scenarios such as music composition and environmental sound morphing, highlighting its adaptability.

Experimental Results

The experimental validation includes timbral morphing for musical instruments, environmental sound morphing, and music morphing. SoundMorpher consistently outperformed the baseline methods across various metrics, including Fréchet audio distance, perceptual similarity, and timbre space smoothness.

Notably, SoundMorpher showed robustness even with different musical instruments and complex environmental sounds, suggesting its flexibility and high quality in dynamic and static morphing tasks. The paper also discusses the model's comparison with concurrent methods, emphasizing its superior performance in perceptual smoothness and intermediateness.

Implications and Future Prospects

The implications of this research extend to various fields, particularly in creative domains such as music production and film post-production. By offering a method to achieve smooth and perceptually consistent audio morphing, SoundMorpher lays the groundwork for future enhancements in auditory scene synthesis and interactive audio systems, potentially driving new applications in AR/VR environments.

Looking forward, further exploration into optimizing model parameters and extending the approach to handle larger semantic gaps between sounds could enhance the smoothness and realism of the transformations. Additionally, investigating more efficient inference techniques could mitigate current computational demands.

In conclusion, SoundMorpher represents a significant advancement in sound morphing technologies, providing a structured and scalable solution that meets the demands of contemporary audio generation tasks. This work not only offers practical applications but also serves as a benchmark for future research endeavors in sound perception and generation using diffusion models.