Class-Aware SDR Improvement

• The metric CA-SDRi extends traditional SDR improvement by averaging class-wise SDR enhancements only when the source is correctly classified.
• It integrates advanced audio features, attention mechanisms, and agent-based error correction to optimize separation and classification in polyphonic mixtures.
• Benchmarking shows up to 14.7% relative improvement over baselines, making CA-SDRi critical for tasks like multi-speaker separation and sound scene analysis.

Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi) quantifies the degree to which audio source separation and classification systems can enhance signal quality for distinct semantic or class categories within polyphonic mixtures. By integrating separation fidelity and class-specific accuracy, this metric supports the evaluation and optimization of models in tasks such as spatial semantic segmentation, multi-speaker separation, and sound scene analysis.

1. Definition and Metric Formulation

Class-Aware Signal-to-Distortion Ratio Improvement (CA-SDRi) extends conventional SDRi by accounting for class membership in the evaluation. For a set of semantic/classes $C$ present in the ground-truth mixture, and a set of predicted classes $\hat{C}$, the CA-SDRi metric is computed by averaging the SDR improvement (SDRi) for those classes correctly identified as present: $$\mathrm{CA\text{-}SDRi} = \frac{1}{|C \cup \hat{C}|} \sum_{k \in (C \cup \hat{C})} P_k$$ where $P_k$ is the SDRi for class $k$ if correctly predicted ($k \in C \cap \hat{C}$), and zero otherwise. This composite metric penalizes both false positives and false negatives, ensuring that improvements in signal quality are only recognized when the separation and classification are correctly aligned (Kwon et al., 17 Sep 2025).

2. Loss Functions and Architectural Strategies

Traditional objective functions for source separation optimization—such as $\ell_1$ or $\ell_2$ norms, Itakura-Saito divergence, or STOI—quantify the similarity between the clean signal and the estimate, but do not explicitly target noise reduction or class-aware enhancement (Nakajima et al., 2018). Signal-to-distortion ratio (SDR) as an objective function allows direct optimization toward noise suppression and recovery of class-specific sources. SDR is defined as: $$\mathrm{SDR} = 10 \log_{10} \left( \frac{ \| \mathbf{s}_{\mathrm{target}} \|^2 }{ \| \hat{\mathbf{s}} - \mathbf{s}_{\mathrm{target}} \|^2 } \right )$$ where $\mathbf{s}_{\mathrm{target}}$ is the component of the estimate lying in the span of the clean signal and its delays. In recent frameworks, automatic differentiation is employed to compute gradients through the SDR computation, facilitating end-to-end optimization (Nakajima et al., 2018).

Class-aware strategies utilize feature fusion, attention mechanisms, and clustering techniques to link separation masks and embeddings with semantic class assignments. For example, attention-based SENet encoders reweight hybrid channel statistics, channeling information essential for separating sources relevant to specific classes (Xiao et al., 2020).

3. Model Components for CA-SDRi Optimization

Recent competitive systems integrate three principal components:

• Universal Sound Separation (USS): Decomposes mixtures into isolated object-level sources, enabling subsequent class-specific extraction (Kwon et al., 17 Sep 2025).
• Single-label Classification (SC): Assigns class labels to each separated source using classifiers equipped with energy-based silence detection and class-specific thresholds.
• Target Sound Extraction (TSE): Refines extraction for each class, conditioned on both the separated waveform and the predicted class label; conditioning is achieved via feature-wise linear modulation (FiLM) and direct waveform injection.

An iterative refinement loop improves both separation and labeling accuracy by feeding back results from each extraction and classification iteration until convergence (Kwon et al., 17 Sep 2025). This approach is particularly suited for complex mixtures with overlapping sources and ambiguous semantic boundaries.

4. Audio Feature Engineering and Error Correction

Enhanced input representations are instrumental in boosting class discrimination and separation fidelity. Key strategies include:

• Spectral Roll-off: Captures the boundary frequency below which a fixed proportion of energy accumulates, enabling detection of high-frequency transients typical of certain classes.
• Chroma Features: Encode tonal and harmonic structure, facilitating separation of acoustically similar but semantically distinct events.

These are concatenated with mel-spectrogram embeddings to enrich the input to classification and separation modules (Park et al., 26 Jun 2025). Agent-based label correction mechanisms, which perform post-hoc relabeling of estimated sources, systematically reduce false positives by reclassifying outputs and removing conflicts, further optimizing CA-SDRi with minimal recall loss.

5. Dataset Design and Refinement

Dataset refinement is shown to be pivotal in maximizing class-aware performance. Audio samples shorter than key duration thresholds (e.g., 1.5 seconds) and perceptually heterogeneous instances are removed to minimize ambiguity and improve class assignment. Undersampled and confounded classes are augmented with high-fidelity samples from external sources such as AudioSet, counteracting bias and overfitting (Park et al., 26 Jun 2025).

6. Comparative Results and Implications

Benchmark results from DCASE 2025 Task 4 demonstrate the impact of these strategies. The integration of spectral roll-off and chroma features, agent-based error correction, and dataset refinement yielded up to 14.7% relative improvement in CA-SDRi over the baseline. The multi-stage self-guided system achieved an overall CA-SDRi of 11.00 dB, outperforming conventional single-pass architectures (e.g., ResUNetK) by 4.4 dB and attaining the highest classification accuracy among all challenge submissions (Kwon et al., 17 Sep 2025). False positive penalized accuracy metrics were also introduced to guide model selection in the context of CA-SDRi optimization (Park et al., 26 Jun 2025).

7. Broader Context and Future Directions

Class-aware SDR improvement methodologies are broadly applicable to spatial semantic segmentation, multi-speaker separation, and general sound scene analysis. Key future directions include integration with multi-channel and reverberant environments—where convolutional filter-invariant SDR criteria (CI-SDR) and minimum variance independent component analysis (MVICA) offer robust performance (Boeddeker et al., 2020, Gu et al., 2021). Future work is likely to focus on scalable architectures for practical acoustic environments, refined semantic modeling, and the unification of separation and classification objectives to further close the gap between signal enhancement and semantic scene analysis.