Spatial-CLAP: Spatial Audio-Text Embeddings

• Spatial-CLAP is a framework for spatially-aware audio–text embeddings that fuses semantic content with precise spatial location in multi-source scenarios.
• It integrates a pretrained monaural content encoder with a content-aware spatial encoder adapted from SELD architectures to capture inter-channel differences.
• Spatial contrastive learning ensures correct content–spatial pairing by leveraging hard negatives, significantly improving retrieval and captioning accuracy.

Spatial-CLAP is a framework for spatially-aware audio–text embeddings designed specifically to address the challenges of representing and binding both the semantic content and spatial attributes of sound sources under multi-source conditions. Unlike conventional audio–text models, which predominantly operate on monaural or single-source audio and thus cannot capture spatial information, Spatial-CLAP introduces a content-aware spatial encoder and a spatial contrastive learning strategy to robustly bind content to its spatial origin. This establishes a new paradigm for embedding learning in realistic, multi-source acoustic environments (Seki et al., 18 Sep 2025).

1. Motivation and Key Challenges

Spatial-CLAP was developed to resolve a core limitation of existing contrastive language–audio pretraining (CLAP) systems: the absence of spatial awareness. Prior models, even those employing multichannel inputs, primarily learn what is present in the audio signal but not where each sound is coming from—especially under multi-source conditions where several sources are active and spatial–content binding is fundamentally ambiguous. In realistic spatial scenes, it is necessary to establish a correspondence between each sound event (content) and its associated spatial descriptor (e.g., direction of arrival).

The principal challenge arises because spatial and content information cannot be separated without risking permutation ambiguities: swapping the spatial contexts of sources leads to entangled or uninterpretably mixed representations in standard architectures. Therefore, learning to bind content and spatial information in multi-source mixtures is fundamentally more difficult than the conventional single-source setting.

2. Content-Aware Spatial Encoder (CA-SE)

Spatial-CLAP introduces a dual-branch audio encoder consisting of a monaural content encoder (CE) and a content-aware spatial encoder (CA-SE):

• The content encoder (CE) is inherited from a pretrained monaural CLAP model and operates on the averaged left/right channels to extract semantic descriptors.
• The CA-SE branch is adapted from sound event localization and detection (SELD) architectures and processes stereo audio directly, capitalizing on spatial cues available from inter-channel differences. The CA-SE is pretrained on SELD tasks to ensure its spatial embeddings are inherently coupled to sound event content.

Outputs of the CE and CA-SE are concatenated and input to a multilayer perceptron (MLP), yielding a unified, fixed-dimensional embedding that encodes both the “what” and “where” aspects of the acoustic scene. The CA-SE thus ensures the preservation and coupling of spatial characteristics with semantic audio attributes.

3. Spatial Contrastive Learning (SCL) Training Strategy

To explicitly enforce correct content–spatial correspondences in training, Spatial-CLAP introduces spatial contrastive learning (SCL). SCL extends the standard in-batch InfoNCE loss by incorporating hard negatives constructed via permutation of content–space assignments.

Given two sound sources $x_1(t)$, $x_2(t)$ and their associated room impulse responses $h_1(t)$, $h_2(t)$:

• The observed stereo signal is formed as $y(t) = (h_1 * x_1)(t) + (h_2 * x_2)(t)$, where $*$ denotes convolution.
• A hard negative is constructed by permuting the associations: $$y_{\text{SCL}}(t) = (h_2 * x_1)(t) + (h_1 * x_2)(t)$$.

Training batches are augmented with both the correct and swapped mixtures. The contrastive loss is minimized only when the embedding aligns with the original, correct assignment, forcing the model to learn the proper pairing of content and spatial attributes. This directly addresses the permutation problem and leads to embeddings where each content is bound to its precise spatial origin.

Mathematically, correspondence is enforced using appropriately paired and permuted audio–text pairs, with loss terms penalizing incorrect (swapped) assignments.

4. Multi-Source Training and Embedding Evaluation

Spatial-CLAP departs from the conventional paradigm by conducting training (and evaluation) explicitly under multi-source conditions:

• Retrieval and classification tasks are evaluated not only for single-source but importantly for two-source (2-src) and three-source (3-src) mixtures, with all possible combinations of source–location (DoA) pairs represented.
• Content–space assignment accuracy is measured by comparing the similarity of audio and text embeddings for the correct assignment versus all possible permutations.
• In two-source mixtures, standard spatial encoders that treat content and spatial features separately perform at chance in disambiguating source–location binding due to permutation ambiguity. In contrast, Spatial-CLAP’s CA-SE branch and SCL training yield significantly improved assignment accuracy, demonstrating robust binding.

For three-source mixtures (never seen in training), content–space assignment accuracy with Spatial-CLAP is 41.77% (chance: 16.31%), significantly outperforming conventional models.

5. Downstream Task Performance and Generalization

Spatial-CLAP embeddings are evaluated on several downstream tasks:

• Audio-to-text and text-to-audio retrieval (R@1): Spatial-CLAP outperforms monaural and conventional (content- and spatial-encoder) baselines, particularly in two-source conditions where binding is critical.
• Spatial classification: Using the embedding to select the correct DoA or DoA pair via similarity, Spatial-CLAP achieves higher accuracy than non-coupled methods in multi-source settings.
• Spatial audio captioning: Embeddings are used in prefix-based GPT-2 caption generation. Standard metrics (BLEU, ROUGE-L, METEOR, CIDEr, SPICE) and spatially-tailored metrics (direction-wise SBERT, directional inclusion ratio) show that captions conditioned on Spatial-CLAP embeddings more accurately reflect both sound events and their spatial locations.

Critically, these results hold even for settings involving complex, unseen mixtures, confirming the utility and generalization capacity of the content–spatially bound embedding produced by Spatial-CLAP.

6. Mathematical Formalism and Key Losses

The construction of negatives for SCL and the explicit content–space assignment is grounded in precise mathematical formulation. For two-source mixtures: $$y(t) = (h_1 * x_1)(t) + (h_2 * x_2)(t) \ y_{\text{SCL}}(t) = (h_2 * x_1)(t) + (h_1 * x_2)(t)$$ Audio–text pairing is enforced in loss with the InfoNCE objective, where negative samples are all permutations not corresponding to the ground-truth assignment.

7. Implications and Future Directions

Spatial-CLAP establishes the technical necessity of training under multi-source conditions for robust spatial–content binding—single-source training is insufficient to generalize. The introduction of a content-aware spatial encoder coupled with spatial contrastive learning achieves representations resilient to source permutation and scalable to more complex spatial audio scenes.

Future research directions include extension to dynamic or moving sources, refinement of spatially-oriented evaluation metrics, and exploration of more general scene representations beyond binary source–location pairs.

Spatial-CLAP thus represents a methodological advance in the domain of multimodal audio–text embedding, aligning audio events with spatial descriptors and with immediate implications for localization, captioning, retrieval, and human–machine spatial scene understanding (Seki et al., 18 Sep 2025).