S2Accompanist is a 402M-parameter semantic-aware diffusion model that achieves SOTA on the ATTM Grand Challenge benchmark for music accompaniment generation via automated data processing and structure-guided VAE fine-tuning.
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ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
Activation steering at a semantic bottleneck in audio diffusion models achieves state-of-the-art control over musical attributes such as instruments, vocals, and genres.
Qwen3-Omni is a unified multimodal model that achieves open-source SOTA on 32 of 36 audio and audio-visual benchmarks and overall SOTA on 22 without degrading performance on text, image, or video relative to single-modal Qwen counterparts.
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
SongFormer achieves state-of-the-art strict boundary detection and functional label accuracy in music structure analysis by fusing SSL representations and using learned source embeddings on a new 14k-song corpus and expert benchmark.
citing papers explorer
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S2Accompanist: A Semantic-Aware and Structure-Guided Diffusion Model for Music Accompaniment Generation
S2Accompanist is a 402M-parameter semantic-aware diffusion model that achieves SOTA on the ATTM Grand Challenge benchmark for music accompaniment generation via automated data processing and structure-guided VAE fine-tuning.
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Leveraging Artist Catalogs for Cold-Start Music Recommendation
ACARec attends over artist catalogs to generate CF embeddings for new tracks, more than doubling recall and NDCG versus content-only baselines in music recommendation.
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TADA! Tuning Audio Diffusion Models through Activation Steering
Activation steering at a semantic bottleneck in audio diffusion models achieves state-of-the-art control over musical attributes such as instruments, vocals, and genres.
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Qwen3-Omni Technical Report
Qwen3-Omni is a unified multimodal model that achieves open-source SOTA on 32 of 36 audio and audio-visual benchmarks and overall SOTA on 22 without degrading performance on text, image, or video relative to single-modal Qwen counterparts.
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Adopting State-of-the-Art Pretrained Audio Representations for Music Recommender Systems
Pretrained audio models show large performance gaps between standard MIR tasks and music recommendation in both hot and cold-start settings.
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Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity
Separating acoustic and expectation ANN representations as teacher targets improves EEG music identification beyond baselines and seed ensembles.
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Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
TASTE dataset and MuQ-token aggregation enable effective use of audio features from large music models to improve content-based music recommendations over collaborative filtering alone.
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SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision
SongFormer achieves state-of-the-art strict boundary detection and functional label accuracy in music structure analysis by fusing SSL representations and using learned source embeddings on a new 14k-song corpus and expert benchmark.