Source-Aware Codec

• Source-aware codec is a neural or hybrid codec that explicitly separates different sound sources using architectures like prompt-driven conditioning and parallel codebooks.
• It employs latent disentanglement and multi-domain reconstruction losses to achieve controlled bitrate allocation and enhanced source-specific fidelity.
• Evaluation protocols use metrics like SI-SDR, ViSQOL, and codebook utilization to benchmark its efficiency, perceptual quality, and forensic traceability.

A source-aware codec is a neural or hybrid audio (or audiovisual) codec that incorporates explicit mechanisms for encoding, separating, or tracing individual sources—by class, domain, or intrinsic factor—within a mixture or composite signal. Unlike conventional codecs that operate on undifferentiated audio, source-aware codecs integrate architectural, quantization, or conditioning strategies to make the encoded representation sensitive to, and optionally disentangled by, sound source categories or content structure. This paradigm targets efficient, controlled, and interpretable compression for downstream tasks such as flexible resynthesis, source-specific reconstruction, forensic attribution, and controllable generation.

1. Principles and Taxonomy of Source-Aware Codec Design

Approaches to source-aware codec construction span several architectural principles:

• Prompt-driven and conditional separation: Injecting user- or model-driven prompts (e.g., <Speech>, <Music>) to steer encoding or decoding with respect to specific sources within a mixture (Aihara et al., 20 Nov 2025, Banerjee et al., 15 Sep 2025).
• Latent domain decomposition: Partitioning codebooks or latent representations according to source class or content factor, ensuring each domain maps to a distinct set of tokens (e.g., using parallel RVQs for speech, music, SFX) (Bie et al., 17 Sep 2024).
• Structural modularity: Architectures may include parallel encoders, per-domain vector quantizers, and composite decoders, allowing source-adaptive bitrate control and selective source enhancement (Yang et al., 2020, Zheng et al., 2 Dec 2024).
• Feature disentanglement: Decomposing intrinsic content (timbre, prosody, linguistic content) into separately quantized streams, promoting disentangled and manipulable latent codes (Zheng et al., 2 Dec 2024).
• Condition-aware generative models: In video or cross-modal settings, diffusion-based decoders conditioned on encoder-specific meta-parameters (e.g., spatial/bit-depth factors) achieve sharp rate–distortion trade-offs (Zhou et al., 2022).

Source awareness can target explicit domain categories, intrinsic voice traits, domain-agnostic semantics, or codec-origin attributes in forensic contexts.

2. Conditioning Mechanisms and Latent Disentanglement

Strategies for injecting source awareness into codec architectures include:

• Prompt injection and FiLM conditioning: SUNAC and CodecSep employ prompt embeddings mapped to FiLM (Feature-wise Linear Modulation) parameters, globally influencing feature representations via transformer blocks and per-channel affine modulations (Aihara et al., 20 Nov 2025, Banerjee et al., 15 Sep 2025). In SUNAC, prompt tokens are concatenated with encoder features, passed through transformers, and then used for per-prompt conditioning through FiLM, enabling the network to target and reconstruct any subset or permutation of sources present in the input mixture.
• Parallel or gated RVQ codebooks: SD-Codec assigns distinct RVQs to each source class. During training, all source-specific reconstructions are supervised via multi-output losses, forcing emergent disentanglement in latent code space without explicit domain labels. The decoder supports both source-specific and summed mixture reconstruction (Bie et al., 17 Sep 2024).
• Encoder decomposition and intra-stream quantization: FreeCodec factorizes speech into timbre (ECAPA-TDNN), prosody, and content streams, each mapped onto dedicated codebooks or left continuous. Quantizer configuration (group VQ, plain VQ) allows precise control of bitrate allocation and source-specific tokenization (Zheng et al., 2 Dec 2024).
• Hard code masking and entropy regularization: SANAC implements an explicit latent split, segmenting the encoder output into disjoint speech and noise sub-codes, each quantized and decoded independently. Bitrates can be dynamically allocated via entropy regularization and explicit ratio losses (Yang et al., 2020).

These mechanisms facilitate robust source separation, selective source inclusion, source-conditioned resynthesis, and controllable bitrate usage.

3. Loss Formulations and Training Objectives

Training objectives for source-aware codecs integrate multi-purpose reconstruction and disentanglement losses:

• Multi-output, multi-domain reconstruction: Losses are computed for each requested source and (optionally) their mixture, typically as weighted sums over domains (Aihara et al., 20 Nov 2025, Bie et al., 17 Sep 2024). For waveform resynthesis and separation, SI-SDR and ViSQOL are standard fidelity/perceptual metrics.
• Permutation-invariant training (PIT): When multiple sources of the same class are present (e.g., multi-speaker speech separation), PIT restricts permutation search within same-class prompts, maximizing SI-SDR across assignments and computing losses only under the optimal permutation (Aihara et al., 20 Nov 2025).
• Adversarial and feature-matching components: Generator losses frequently include adversarial terms from multi-scale STFT discriminators, as well as feature-matching penalties to enforce perceptual quality and stabilize adversarial training (Aihara et al., 20 Nov 2025, Bie et al., 17 Sep 2024, Zheng et al., 2 Dec 2024).
• Commitment and codebook losses: Vector quantizer-specific objectives (commitment loss and codebook update loss) constrain the encoder’s outputs to remain close to the selected codebook entries, enforcing codebook utilization and compactness (Aihara et al., 20 Nov 2025, Bie et al., 17 Sep 2024, Zheng et al., 2 Dec 2024).
• Disentanglement (semantic) losses: Additional losses, e.g. cosine similarity with SSL-based embeddings for content or hyperbolic total-correlation regularization, drive independent latent factorization and parameter regression for structured source attribution (Zheng et al., 2 Dec 2024, Phukan et al., 14 Jun 2025).

Optimization typically alternates between encoder–decoder modules, discriminators, and, when present, masking or prompt modules.

4. Representative Architectures and Implementations

Key realised source-aware codecs and decoder-integrated pipelines include:

Model	Source Disentanglement	Conditioning Mechanism	Bitrate (kbps)	Notable Features
SUNAC	Prompt-driven, unified	Cross-prompt + FiLM	6 (fixed)	Arbitrary user-prompt query, PIT for same-class (Aihara et al., 20 Nov 2025)
SD-Codec	Class-based, explicit	Parallel RVQs	6/source	Per-domain codebook, interpretable latent (Bie et al., 17 Sep 2024)
CodecSep	Free text query	CLAP+FiLM Masker	Codec-limited	Latent masking, end-to-end bitstream, universal separation (Banerjee et al., 15 Sep 2025)
SANAC	Speech/noise split	Hard-masked code split	Dynamic (ratio-reg.)	Source-adaptive entropy control (Yang et al., 2020)
FreeCodec	Intrinsic factors	Parallel encoders, VQ	≈0.5	Distinct timbre/prosody/content streams (Zheng et al., 2 Dec 2024)
CaDM (video)	ENCODER-aware dec.	Resolution/bit-depth cond.	Variable (video)	Decoder aware of encoding settings via explicit upsampled hint (Zhou et al., 2022)
HYDRA (NACSP)	Forensic parsing	Hyperbolic multi-task	(for parsing, not coding)	Structured regression of codec parameters (Phukan et al., 14 Jun 2025)

Notably, source-awareness emerges both in specialized codebook routing (SD-Codec), prompt-guided conditional masking or modulation (SUNAC, CodecSep), and multi-head hyperbolic regression heads for forensic tasks (HYDRA).

5. Evaluation Protocols and Performance Metrics

Benchmarking involves both standard resynthesis/separation scores and operational efficiency assessments:

• SI-SDR and ViSQOL: Widely used for waveform fidelity (scale-invariant SDR, dB) and perceptual quality (ViSQOL).
• Closed/open-set attribution F1/AUC/EER: For source-tracing in forensic contexts (Neural Codec Source Tracing, NCST), macro F1 for in-distribution, ROC-based metrics for open-set (Xie et al., 11 Jan 2025).
• Token and bitrate analysis: Quantifies operational efficiency (tokens/sec, bits/sec), especially relevant for extremely low-bitrate codecs (FreeCodec 0.45 kbps) (Zheng et al., 2 Dec 2024).
• Rate–distortion curves: In video, FID/SSIM and bitrate are compared against existing super-resolution and neural video codecs (CaDM achieves up to ≈21× bitrate reduction at superior FID) (Zhou et al., 2022).
• Compute and latency: GMACs per inference; e.g., CodecSep provides ≈54× compute reduction over spectrogram-domain baseline pipelines (Banerjee et al., 15 Sep 2025).
• Subjective preference and intelligibility (MUSHRA, UTMOS, STOI): Selected for speech codecs, often at parity or surpassing conventional codecs and neural baselines at much lower bitrates (Zheng et al., 2 Dec 2024, Yang et al., 2020).

Representative results show that source-aware codecs can match or surpass cascaded pipelines (e.g., separation-then-coding) at a fraction of the computational cost, and can generalize to arbitrary mixture queries.

6. Downstream Applications and Implications

Source-aware codecs enable advanced capabilities across several domains:

• User-driven selective resynthesis: SUNAC allows encoding or decoding only the sources (or source types) of interest, reducing downstream traffic and focusing analytical workflows (Aihara et al., 20 Nov 2025).
• Cross-source editing and manipulation: Separate latent streams or tokens per source type (domain or intrinsic factor) enable independent manipulation, mixing, or replacement—crucial for generative speech/music tasks and voice conversion (Zheng et al., 2 Dec 2024, Bie et al., 17 Sep 2024).
• Semantic analysis and transcription: Codecs like FreeCodec provide content-specific codes aligned to SSL embeddings, facilitating ASR and speaker diarization on the compressed representation (Zheng et al., 2 Dec 2024).
• Forensic tracing and attribution: Open-set source parsing (NACSP) via regression (HYDRA) yields interpretability essential for deepfake provenance and chain-of-custody analysis (Phukan et al., 14 Jun 2025, Xie et al., 11 Jan 2025).
• Rate–distortion and efficiency trade-offs: In streaming and low-bandwidth contexts, source-aware codecs can outperform standard approaches by compressing non-salient sources or source types more aggressively (e.g., SANAC, CaDM) (Yang et al., 2020, Zhou et al., 2022).

A plausible implication is that source-aware coding may soon become a basis for auditability and control in both media compression and synthetic content authentication.

7. Limitations, Open Challenges, and Future Directions

Several challenges persist in the development and deployment of source-aware codecs:

• Robustness to unseen real-world data: NCST models exhibit severe drops in unseen real audio, with generalization remaining an open technical hurdle. Domain-invariant feature learning and larger annotated corpora are suggested mitigations (Xie et al., 11 Jan 2025).
• Open-set attribution granularity: Conventional classifiers can only output “unknown” for unseen codecs; parameter regression (NACSP/HYDRA) partially addresses this, but identification of fine-grained intra-family variants remains limited (Phukan et al., 14 Jun 2025).
• Handling arbitrary source permutations and content leakage: Ensuring consistent source–token assignments across class permutations and preventing source–attribute entanglement requires intricate training schemes and may benefit from future architectural innovations.
• Real-time, on-device execution: Scaling prompt-driven separation and coding to edge devices necessitates lightweight models (e.g., CodecSep’s 1.35 GMACs) and compact interface contracts (Banerjee et al., 15 Sep 2025).
• Beyond audio: cross-modal and generative extensions: The source-aware principle extends naturally to other modalities, as evidenced by CaDM for video (Zhou et al., 2022), but joint audio–visual or multi-modal codecs are a prospective direction.
• Continual dataset updates and adaptive models: For forensic systems, ongoing curation of codec and ALM variants is essential to maintain attribution efficacy.

Continued advances in source-aware encoding—by fusing conditioning, disentanglement, and generative models—are poised to yield codecs that are not only efficient but verifiably interpretable, controllable, and robust across communication, synthesis, and security applications.