arXiv:2606. 10010v1 Announce Type: cross Abstract: Evaluating text-to-music (TTM) systems remains expensive because music impression (MI) and text alignment (TA) scores rely on human mean opinion scores (MOS).
arXiv:2603. 00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind.
arXiv:2608.23484v1 Announce Type: new Abstract: We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a mu...
We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a multi-modal and personalized conversational recomme...
arXiv:2606. 17006v1 Announce Type: cross Abstract: We introduce TuneJury, an open, instance-level pairwise reward model for text-to-music that predicts a music preference score from a text prompt and an audio clip.
arXiv:2606. 07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages.
MUUNRiver-Bench is a diagnostic benchmark for music retrieval that uses natural‑language instructions to define relevance for reference‑audio queries. It contains 3,440 tracks across 13 genres and 116 sub‑genres and covers seven tasks such as similar‑music, style‑preserving lyric‑rewriting, cover, and segment retrieval. Experiments with six models in eight configurations show that acoustic encoders favor local identity while text‑aligned encoders favor semantic relations, and that instruction‑aware and audio‑text fusion systems do not consistently outperform their backbones.
TTM-Bench is a framework designed to benchmark text-to-music systems by establishing a common protocol for reproducible evaluation. It measures performance along two axes: musical-content alignment—assessed through semantic, genre, and musical-descriptor agreement scores against a shared musical specification—and computational efficiency, which includes generation latency, real-time factor, resource usage for local models, and cost for hosted services. A preliminary case study using TTM-Bench shows that higher alignment does not necessarily mean lower computational demands, underscoring the need for distinct, interpretable metrics.
arXiv:2606. 00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content.
The paper introduces STeReO, a reranker that orchestrates speech and text retrievers to aggregate evidence from heterogeneous databases. It addresses the scarcity of training data by curating a dataset of queries, mixed-modality evidence, and relevance rankings, then trains and evaluates the reranker in both single- and mixed-modality settings. Results show that STeReO effectively selects the most relevant evidence, leading to significant improvements in downstream question‑answering performance.
arXiv:2608. 14819v1 Announce Type: cross Abstract: Music foundation models are commonly used as frozen audio feature extractors, yet selecting which layer to extract from remains largely heuristic.
arXiv:2511. 05550v3 Announce Type: replace-cross Abstract: Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio.
arXiv:2607. 06929v1 Announce Type: cross Abstract: Music aesthetic assessment is a challenging yet underexplored problem, requiring models to capture fine-grained, multi-dimensional human perceptual judgments.