arXiv:2604.04025v1 Announce Type: new Abstract: Background music shapes attention, affect, and approach behavior in commercial environments, yet the neural plausibility of AI-generated music for such settings remains poorly characterized. We present an in-silico pilot study that combines Wubble, a generative music system, with TRIBE v2, a publicly released whole-brain encoding model, to estimate cortical response profiles for prompt-conditioned retail music. Five fully instrumental tracks were generated to span low-to-high arousal, sparse-to-dense arrangement, and neutral-to-positive valence prompts, then analyzed with audio-only TRIBE v2 inference on loudness-normalized waveforms. Analysis focused on fsaverage5 cortical predictions summarized over auditory, superior temporal, temporo-parietal, and inferior frontal HCP parcels. The fast bright major-pop condition produced the largest whole-cortex mean activation (0.0402), the strongest prefrontal ROI composite response (0.0704), and the highest parcel means in IFJa (0.1102), IFJp (0.0995), A5 (0.0188), and area 45 (0.0015). Pairwise spatial correlations ranged from 0.787 to 0.974, indicating that prompt variation modulated predicted cortical states rather than yielding a single undifferentiated response profile. Predicted cortical surface maps further revealed visually distinct spatial organization between low-arousal and high-arousal conditions. These results support a cautious claim of cortical neurological plausibility: prompt-conditioned AI music can systematically shift predicted auditory-temporal-prefrontal patterns relevant to salience and valuation. Although the study does not establish subcortical reward engagement or consumer behavior, it provides a reproducible framework for neural pre-screening and pre-optimization of commercial music generation against biologically informed cortical proxies.

arXiv:2604.03329v1 Announce Type: cross Abstract: Violence detection benefits from audio, but real-world soundscapes can be noisy or weakly related to the visible scene. We present CoLoRSMamba, a directional Video to Audio multimodal architecture that couples VideoMamba and AudioMamba through CLS-guided conditional LoRA. At each layer, the VideoMamba CLS token produces a channel-wise modulation vector and a stabilization gate that adapt the AudioMamba projections responsible for the selective state-space parameters (Delta, B, C), including the step-size pathway, yielding scene-aware audio dynamics without token-level cross-attention. Training combines binary classification with a symmetric AV-InfoNCE objective that aligns clip-level audio and video embeddings. To support fair multimodal evaluation, we curate audio-filtered clip level subsets of the NTU-CCTV and DVD datasets from temporal annotations, retaining only clips with available audio. On these subsets, CoLoRSMamba outperforms representative audio-only, video-only, and multimodal baselines, achieving 88.63% accuracy / 86.24% F1-V on NTU-CCTV and 75.77% accuracy / 72.94% F1-V on DVD. It further offers a favorable accuracy-efficiency tradeoff, surpassing several larger models with fewer parameters and FLOPs.

arXiv:2604.03333v1 Announce Type: cross Abstract: Symbolic music generation has made significant progress, yet achieving fine-grained and flexible control over composer style remains challenging. Existing training-based methods for composer style conditioning depend on large labeled datasets. Besides, these methods typically support only single-composer generation at a time, limiting their applicability to more creative or blended scenarios. In this work, we propose Composer Vector, an inference-time steering method that operates directly in the model's latent space to control composer style without retraining. Through experiments on multiple symbolic music generation models, we show that Composer Vector effectively guides generations toward target composer styles, enabling smooth and interpretable control through a continuous steering coefficient. It also enables seamless fusion of multiple styles within a unified latent space framework. Overall, our work demonstrates that simple latent space steering provides a practical and general mechanism for controllable symbolic music generation, enabling more flexible and interactive creative workflows. Code and Demo are available here: https://github.com/JiangXunyi/Composer-Vector and https://jiangxunyi.github.io/composervector.github.io/

arXiv:2604.04229v1 Announce Type: cross Abstract: Learning aligned multimodal embeddings from weakly paired, label-free corpora is challenging: pipelines often provide only pre-extracted features, clips contain multiple events, and spurious co-occurrences. We propose HSC-MAE (Hierarchical Semantic Correlation-Aware Masked Autoencoder), a dual-path teacher-student framework that enforces semantic consistency across three complementary levels of representation - from coarse to fine: (i) global-level canonical-geometry correlation via DCCA, which aligns audio and visual embeddings within a shared modality-invariant subspace; (ii) local-level neighborhood-semantics correlation via teacher-mined soft top-k affinities, which preserves multi-positive relational structure among semantically similar instances; and (iii) sample-level conditional-sufficiency correlation via masked autoencoding, which ensures individual embeddings retain discriminative semantic content under partial observation. Concretely, a student MAE path is trained with masked feature reconstruction and affinity-weighted soft top-k InfoNCE; an EMA teacher operating on unmasked inputs via the CCA path supplies stable canonical geometry and soft positives. Learnable multi-task weights reconcile competing objectives, and an optional distillation loss transfers teacher geometry into the student. Experiments on AVE and VEGAS demonstrate substantial mAP improvements over strong unsupervised baselines, validating that HSC-MAE yields robust and well-structured audio-visual representations.