Deep Cross-Modal Audio-Visual Generation (1704.08292v1)

Abstract: Cross-modal audio-visual perception has been a long-lasting topic in psychology and neurology, and various studies have discovered strong correlations in human perception of auditory and visual stimuli. Despite works in computational multimodal modeling, the problem of cross-modal audio-visual generation has not been systematically studied in the literature. In this paper, we make the first attempt to solve this cross-modal generation problem leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks to achieve cross-modal audio-visual generation of musical performances. We explore different encoding methods for audio and visual signals, and work on two scenarios: instrument-oriented generation and pose-oriented generation. Being the first to explore this new problem, we compose two new datasets with pairs of images and sounds of musical performances of different instruments. Our experiments using both classification and human evaluations demonstrate that our model has the ability to generate one modality, i.e., audio/visual, from the other modality, i.e., visual/audio, to a good extent. Our experiments on various design choices along with the datasets will facilitate future research in this new problem space.

• The paper introduces a novel conditional GAN framework for cross-modal audio-visual synthesis, improving both sound-to-image and image-to-sound generation.
• It demonstrates superior performance of the classification-based sound encoding (S2I-C) over autoencoder-based models, with high fidelity in instrument-specific image generation.
• The study offers promising implications for creative arts and assistive technologies by enabling robust, bidirectional sensory content conversion.

An Expert Analysis of "Deep Cross-Modal Audio-Visual Generation"

In "Deep Cross-Modal Audio-Visual Generation," Chen et al. address the multifaceted challenge of generating cross-modal audio-visual content using conditional generative adversarial networks (GANs). This paper pioneers the systematic exploration of cross-modal generation, focusing on producing one modality from another, i.e., converting audio into visuals and vice versa. Through this endeavor, the authors extend the applicability of GANs, proposing novel architectures and training strategies to tackle this dual complex generation mechanism.

Technical Contributions

The principal technical contribution of this paper is the introduction and validation of conditional GAN architectures for cross-modal audio-visual generation. The work is segmented into two primary conversion tasks: Sound-to-Image (S2I) and Image-to-Sound (I2S) generation.

• S2I Generation: This task is evaluated at two levels: instrument-oriented and pose-oriented. For the instrument-oriented generation, a single model generates musical performance images across various instruments, maintaining fidelity to the auditory input. The pose-oriented generation aims at producing accurate human poses corresponding to specific sound cues within a single instrument domain, showcasing the model's potential in capturing fine-grained audio-visual correlations.
• I2S Generation: Here, the model synthesizes audio spectrograms from images of musical performances. The conversion accuracy underscores the complexity of transforming static visual cues into dynamic auditory representations, a task far less explored than the converse.

The authors employ several model variations, each subjected to different training regimens and encoder configurations. The S2I-C network emerged as the superior model, characterized by its classification-based approach to sound encoding that leverages CNNs for dimensionality reduction and feature extraction, thereby providing robust conditional inputs to the GAN structure.

Empirical Evaluation

The empirical results, presented through both quantitative and human evaluations, support the efficacy of the proposed models. Human evaluators found over half of the S2I-C generated images to be realistically aligned with the corresponding auditory inputs. In contrast, models utilizing autoencoder-derived sound features, such as the S2I-A network, demonstrated markedly lower performance.

Classification-based evaluation further substantiates these findings, with the S2I-C achieving high accuracy when tested on a pre-trained image classifier, thus verifying that generated images retained discernible, instrument-specific features. This contrasts with the autoencoder-based models, signifying the importance of expressive, discriminative latent representations in cross-modal tasks.

Broader Implications and Future Prospects

The implications of reliable cross-modal generation are profound across numerous sectors, including enhanced human-computer interaction, creative arts synthesis, and assistive technologies, notably in contexts where one sensory modality is more accessible than another. For instance, automated and innovative art generation or aiding individuals with sensory impairments through cross-modal aids could see substantive advancements stemming from robust cross-modal generation systems.

Future research directions could focus on refining the I2S generation task, where the current accuracy lags behind S2I. Improving the sound synthesizing process, possibly by integrating advanced sound encoding techniques or leveraging more extensive datasets, could lead to substantial improvements. Additionally, refinement of training protocols could enhance the GANs' ability to capture and generate nuanced modalities, particularly in complex and dynamically rich scenarios.

In conclusion, the work by Chen et al. establishes a foundational framework and opens new avenues for exploration in cross-modal generation using GANs, setting a precedent for future work in this promising interdisciplinary field.