GRAM: Spatial general-purpose audio representations for real-world environments
Abstract
Audio foundation models learn general-purpose audio representations that facilitate a wide range of downstream tasks. While the performance of these models has greatly increased for conventional single-channel, dry audio clips, their success in real-world acoustic environments with reverberation and noise is limited. Furthermore, most audio foundation models ignore the spatial dimension of real-world acoustic environments, ruling out tasks involving sound localization. To address these limitations, we propose GRAM: a general-purpose real-world audio model that employs a multi-channel masked autoencoder to efficiently learn spatial audio representations. We evaluated GRAM and other audio foundation models in a standardized manner on high-quality simulations of naturalistic, spatial acoustic environments as well as recordings of real-world environments and release these two complementary benchmark task suites: NatHEAR and RealSELD. Our results demonstrate that GRAM outperforms all state-of-the-art self-supervised audio foundation models on NatHEAR and the clean, single-channel version HEAR, while using only a fraction of the training data. GRAM also shows state-of-the-art localization performance in simulated environments and generalizes efficiently to real-world recordings in RealSELD. Taken together, GRAM presents a significant advance toward robust spatial audio foundation models for real-world environments.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents GRAM, a significant advancement in spatial audio representation, demonstrating state-of-the-art performance in real-world environments while addressing the limitations of existing audio foundation models. The comprehensive methodology and rigorous evaluation contribute to its potential impact on the field of machine learning and audio processing.
Methodology
The paper presents GRAM, a multi-channel masked autoencoder designed to learn spatial audio representations. The methodology is well-structured, employing a novel training pipeline that utilizes high-quality simulations of real-world sound environments. The use of a masked autoencoder to reconstruct spatial audio features is innovative, particularly in the context of audio foundation models, which typically overlook spatial dimensions. The introduction of two benchmark suites, NatHEAR and RealSELD, adds significant value by providing standardized evaluation metrics for audio models in complex environments.
Results & evaluation
The experiments are comprehensive, comparing GRAM against state-of-the-art models across various tasks in both simulated and real-world environments. The results demonstrate GRAM's superior performance in sound localization and general-purpose audio representation tasks, achieving state-of-the-art results while requiring less training data. The inclusion of ablation studies further strengthens the evaluation by providing insights into the impact of different model components and training strategies.
Reproducibility
The paper provides sufficient details regarding the training process, model architecture, and evaluation metrics, which enhances reproducibility. The authors have made their code and datasets available, which is a positive aspect for the community. However, some specific hyperparameter settings and configurations could be more explicitly detailed to facilitate easier replication of results.
Limitations
One limitation noted is the inadequate resolution of mel-spectrograms for binaural inputs, which may have impacted localization performance. Additionally, while the model shows promise in real-world applications, its performance in highly complex acoustic environments with significant noise interference remains to be fully explored.
Broader impact
The advancements made by GRAM could significantly impact various applications, including audio-visual scene understanding, robotics, and ambient intelligence systems. By improving the robustness of audio models in real-world environments, this work could enhance user experiences in smart environments and contribute to the development of more sophisticated auditory perception systems. The paper presents GRAM, a significant advancement in spatial audio representation, demonstrating state-of-the-art performance in real-world environments while addressing the limitations of existing audio foundation models. The comprehensive methodology and rigorous evaluation contribute to its potential impact on the field of machine learning and audio processing.
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