Audio Semantic Representations

• Audio-based semantic representations are vector encodings that capture high-level sound events and scene information, prioritizing meaning over waveform fidelity.
• They employ supervised and unsupervised techniques, such as contrastive learning and vector quantization, to extract discrete semantic tokens aligned with audio tasks.
• These representations power applications like automated audio captioning, zero-shot classification, and audio-text retrieval, demonstrating competitive performance against acoustic methods.

Audio-based semantic representations are vectorial or discrete encodings of audio signals designed to capture high-level, event-, scene-, or object-related information, emphasizing the meaning or function of sounds over their low-level waveform details. These representations provide the foundation for a broad class of tasks, including automated audio captioning, retrieval, zero-shot classification, source separation, and multimodal reasoning. They stand in contrast to acoustic representations that primarily target signal fidelity. The audio semantics community has developed a spectrum of models and evaluation frameworks, ranging from unsupervised contrastive learning and vector quantization of self-supervised features, to explicitly supervised, label-aligned tokenizers and joint audio–text embedding architectures.

1. Taxonomy: Semantic vs. Acoustic Tokens

Audio representations can be categorized into semantic and acoustic tokens. Acoustic tokens, generated via neural audio codecs (e.g., EnCodec, DAC), are optimized for waveform reconstruction: they encode local time–frequency information to ensure decoded audio is perceptually close to the input, minimizing a loss of the form $$L_\text{rec} = \|x_{\text{raw}} - \text{Decoder}(\text{Quantizer}(\text{Encoder}(x_{\text{raw}})))\|^2$$ (Tian et al., 21 May 2025). In contrast, semantic tokens aim to preserve high-level sound information—event class, source identity, scene type—at the expense of exact waveform fidelity. These are obtained by discretizing intermediate representations of large audio models (either through k-means clustering or (Residual) Vector Quantization) operating on features from supervised or self-supervised models trained for audio understanding.

Token Type	Extraction Mechanism	Main Objective	Example Models
Acoustic	Neural codec (EnCodec, DAC)	Waveform reconstruction	EnCodec, DAC
Semantic	K-means, VQ on SSL/Tagging	Semantic preservation	RepCodec, BEATs, CLAP

Semantic tokens can be constructed in an unsupervised manner (discretizing SSL features without category labels) or in a supervised way, e.g., training a tokenizer with an explicit audio tagging objective so that the latent space is informed by event-level semantics (Tian et al., 21 May 2025).

2. Approaches to Learning Audio-based Semantic Representations

A. Discrete Semantic Tokenization via Supervised Objectives

The dominant supervised pipeline starts from a pre-trained, semantic-rich encoder (e.g., BEATs audio tagging model). The architecture is partitioned (early layers as Encoder₁, late layers as Encoder₂, both frozen), and a lightweight quantizer ("codec") is inserted in between. This codec comprises a small encoder, a learnable VQ (or RVQ; codebook size typically 1024) layer, and a decoder. Only the codec is updated during supervised training, enforcing multi-label classification over AudioSet event classes with a binary cross-entropy loss. This yields tokenizers that extract discrete semantic tokens highly aligned with downstream semantic tasks (Tian et al., 21 May 2025).

B. Unsupervised and Contrastive Methods

Unsupervised semantic audio representations can be learned using triplet- or contrastive-loss objectives enforcing that:

• Small perturbations (temporal/frequency shifts, additive noise) do not affect semantics;
• Mixtures inherit categories of constituent sounds;
• Temporally proximate events are semantically related.

These drive embeddings to cluster semantically similar sounds without explicit labels, reaching up to 80% of the classification performance of fully supervised models in limited-label regimes (1711.02209).

Siamese and contrastive networks embed audio such that Euclidean or cosine proximity correlates with sound-event or class similarity (Manocha et al., 2017). Advanced models align learned audio features with textual semantics using cross-modal contrastive learning, as in CLAP (Niizumi et al., 28 Mar 2025).

C. Tokenization and Downstream Integration

For sequence tasks (e.g., captioning), semantic tokens are extracted framewise, summed/staked across quantization levels, and fed to encoder–decoder architectures (NLP backbones like BART, GPT-2 XL, Transformers) with downsampling or prefix-tuning modules (Tian et al., 21 May 2025, Takeuchi et al., 1 Jun 2025). Discrete semantic tokens (produced via k-means, VQ, or supervised VQ on SSL features) outperform acoustic tokens and yield competitive captioning results compared to continuous representations.

3. Applications and Evaluation Frameworks

Applications of audio-based semantic representations span:

• Automated Audio Captioning (AAC): Semantic tokens drive LLMs that describe audio events and scenes. Supervised semantic tokenizers (AudioSet-trained VQ/RVQ) deliver near-continuous-level performance (SPIDEr=0.294 for supervised-RVQ, compared to BEATs continuous at 0.299) and consistently outperform conventional acoustic tokenizers (e.g., EnCodec) (Tian et al., 21 May 2025, Takeuchi et al., 1 Jun 2025).
• Zero-shot Audio Classification: Representations computed by models like CLAP, VGGish–Word2Vec, or BERT can be linearly mapped into a semantic embedding space (using margin-based ranking objectives), allowing label inference for unseen classes via dot-product compatibility (Xie et al., 2020). Hybrid embeddings (combining label and sentence-level features) yield best performance (e.g., mAP=0.21 with [GLE;GSE] on AudioSet) (Xie et al., 2020).
• Audio–Text Retrieval and Semantic Communication: Cross-modal retrieval leverages shared audio–text representation spaces; diffusion semantic communication pipelines transmit compressed audio and semantic representations that are robust to channel degradations (Grassucci et al., 2023).
• Interpretable Semantics and Concept-based Decomposition: Post-hoc methods transform dense audio embeddings (from CLAP) into sparse, concept-based vectors using a dictionary of audio tags, producing highly interpretable, human-readable representations. These match or exceed zero-shot accuracy of the original audio embedding on tasks such as ESC-50 and UrbanSound8K (Zhang et al., 18 Apr 2025).
• Voice Conversion and Source Separation: Semantic representations extracted by different pre-training paradigms (ASR, SSL, multitask) emphasize complementary information (lyric, melody, expression); fusing them enables robust singing voice conversion and source separation, where class tokens in transformer architectures carve mixtures into semantically disentangled channels (Zhang et al., 2023, Mo et al., 2024).

4. Comparative Metrics and Performance

The most widely adopted metrics for quantifying semantic representation effectiveness are:

• AAC: SPIDEr (composite of CIDEr and SPICE), FENSE (semantic matching), METEOR, ROUGE-L, CIDEr, unique vocabulary size (#Words).
• Classification: mean average precision (mAP), top-1 accuracy per class, F₁, and retrieval precision at K (P@K).
• Similarity/Retrieval: cosine similarity in embedding space, triplet constraint satisfaction (fraction of correctly ordered triplets for clip similarity), Spearman's ρ with human judgments for word similarity tasks.

Empirically, semantic tokens dramatically outperform acoustic waveform tokens on semantic tasks (SPIDEr: supervised-RVQ=0.294 vs. EnCodec=0.085 (Tian et al., 21 May 2025)); fusion or hybridization of multiple semantic sources provides incremental gains; supervised label-aligned tokenizers recover nearly all the gap to strong continuous models; discrete tokens enable efficient LLM conditioning.

Tokenizer	SPIDEr (↑)	FENSE (↑)
k-means (K=1024)	0.267	0.474
RepCodec-RVQ	0.292	0.487
Supervised-RVQ	0.294	0.490
EnCodec (acoustic)	0.085	–
BEATs (continuous)	0.299	–

5. Insights, Limitations, and Future Prospects

A. Semantics over Fidelity

Semantic representation aligns with the requirements of content-based captioning and retrieval better than high-fidelity reconstruction: competitive AAC performance can be achieved by discarding waveform detail in favor of event-level abstraction. This shift has enabled compact, LLM-friendly bi-modal interfaces for audio-text models (Tian et al., 21 May 2025, Takeuchi et al., 1 Jun 2025).

B. Discretization–Information Trade-off

Discrete audio semantic representations, while more modular and compatible with NLP frameworks, lose some information relative to strong continuous models (e.g., BEATs continuous SPIDEr=0.299 vs. top discrete supervised RVQ 0.294). Explicit semantic supervision via audio-tagging recovers much of this loss (Tian et al., 21 May 2025, Takeuchi et al., 1 Jun 2025).

C. Robustness via Supervision and Fusion

Supervised semantic tokenizers and fusion of multiple pre-trained sources yield better generalization, robustness to background artifacts, and improved factorization of melody, lyrics, and timbral content for complex tasks such as singing voice conversion and universal source separation (Zhang et al., 2023, Mo et al., 2024).

D. Interpretability and Human Alignment

Recent work has enabled post-hoc decomposition of dense audio embeddings into interpretable, concept-based vectors aligned with human-labeled tags, providing transparency in semantic content and supporting zero-/few-shot tasks at accuracy near dense methods (Zhang et al., 18 Apr 2025).

E. Open Challenges

Major challenges include:

• Balancing semantic abstraction with fine-grained details (especially for polyphonic or rapidly varying content)
• Scaling semantic vocabularies and accommodating new sound classes without retraining tokenizers
• Adapting to real-world noise, diverse microphones, and mixture scenarios
• Unified multimodal pretraining objectives spanning audio, text, image, and video for broader grounding.

6. Broader Implications and Future Directions

Audio-based semantic representations are foundational for the next generation of multimodal AI. Their compactness and alignment with language semantics facilitate efficient cross-modal fusion (audio–text, audio–vision), crucial for LLMs with audio conditioning, cross-modal generation, semantic communication, and robust, interpretable decision-making in sound understanding. Training tokenizers using task-aligned supervision offers a general recipe for constructing compact, semantic-centric discrete representations that are highly adaptable across domains and tasks (Tian et al., 21 May 2025, Takeuchi et al., 1 Jun 2025). Theoretical and empirical evidence from neuroscience further suggests their tighter alignment with natural human semantic processing compared to purely textual codes, strengthening their relevance for future cognitively aligned AI systems (Zhang et al., 20 Jan 2026).