Lyric-to-melody generation aims to automatically create melodies based on given lyrics, requiring the capture of complex and subtle correlations between them. However, previous works usually suffer from two main challenges: 1) lyric-melody alignment modeling, which is often simplified to one-syllable/word-to-one-note alignment, while others have the problem of low alignment accuracy; 2) lyric-melody harmony modeling, which usually relies heavily on intermediates or strict rules, limiting model's capabilities and generative diversity. In this paper, we propose SongGLM, a lyric-to-melody generation system that leverages 2D alignment encoding and multi-task pre-training based on the General Language Model (GLM) to guarantee the alignment and harmony between lyrics and melodies. Specifically, 1) we introduce a unified symbolic song representation for lyrics and melodies with word-level and phrase-level (2D) alignment encoding to capture the lyric-melody alignment; 2) we design a multi-task pre-training framework with hierarchical blank infilling objectives (n-gram, phrase, and long span), and incorporate lyric-melody relationships into the extraction of harmonized n-grams to ensure the lyric-melody harmony. We also construct a large-scale lyric-melody paired dataset comprising over 200,000 English song pieces for pre-training and fine-tuning. The objective and subjective results indicate that SongGLM can generate melodies from lyrics with significant improvements in both alignment and harmony, outperforming all the previous baseline methods.

This paper explores AI-mediated human-pet musical co-creation from an interdisciplinary perspective, leveraging recent advancements in animal-assisted therapy. These advancements have shown significant psychosocial benefits, especially in reducing anxiety and enhancing social engagement. Building on these findings, this study innovatively employs pet vocal timbres as 'digital avatars' to enhance emotional investment during the music creation process. We propose PetCoCre, a novel system that applies pet vocal timbres in three distinct character paradigms within AI music creation: (1) PetRhythm: using pet voices as rhythmic percussion through beat synchronization. (2) PetMelody: enabling pet voices to act as melodic instruments via pitch-shifting alignment. (3) PetVocalia: utilizing pet vocal timbres as the target timbre for SVC (Singing Voice Conversion), where the converted singing voice replaces the original singer's voice, thus preserving the original semantic content. Beyond these character paradigms, our technical innovation lies in proposing SaMoye, the first open-source, high-quality zero-shot SVC model that effectively overcomes existing methods' zero-shot limitations by employing mixed speaker embeddings for timbre enhancement and leveraging a large-scale singing voice dataset. In our experiments, we collected dog and cat vocalization data from pet stores and conducted experiments with 30 participants. Results demonstrate that the human-pet co-creation mode led to significant enhancements in pleasure and creative satisfaction compared to solo AI music generation, along with a significant reduction in participants' anxiety levels. Through collaborative art creation, this research pioneers new paradigms for animal-assisted therapeutic interventions and expands the boundaries of AI-assisted creative collaboration.

Singing accent research is underexplored compared to speech accent studies, primarily due to the scarcity of suitable datasets. Existing singing datasets often suffer from detail loss, frequently resulting from the vocal-instrumental separation process. Additionally, they often lack regional accent annotations. To address this, we introduce the Multi-Accent Mandarin Dry-Vocal Singing Dataset (MADVSD). MADVSD comprises over 670 hours of dry vocal recordings from 4,206 native Mandarin speakers across nine distinct Chinese regions. In addition to each participant recording audio of three popular songs in their native accent, they also recorded phonetic exercises covering all Mandarin vowels and a full octave range. We validated MADVSD through benchmark experiments in singing accent recognition, demonstrating its utility for evaluating state-of-the-art speech models in singing contexts. Furthermore, we explored dialectal influences on singing accent and analyzed the role of vowels in accentual variations, leveraging MADVSD's unique phonetic exercises.

Music as an emotional intervention media has important applications in scenarios such as music therapy, games, and movies. However, music needs real-time arrangement according to changing emotions, bringing challenges to balance emotion real-time fit and soft emotion transition due to the fine-grained and mutable nature of the target emotion. Existing studies mainly focus on achieving emotion real-time fit, while the issue of smooth transition remains understudied, affecting the overall emotional coherence of the music. In this paper, we propose REMAST to address this trade-off. Specifically, we recognize the last timestep's music emotion and fuse it with the current timestep's input emotion. The fused emotion then guides REMAST to generate the music based on the input melody. To adjust music similarity and emotion real-time fit flexibly, we downsample the original melody and feed it into the generation model. Furthermore, we design four music theory features by domain knowledge to enhance emotion information and employ semi-supervised learning to mitigate the subjective bias introduced by manual dataset annotation. According to the evaluation results, REMAST surpasses the state-of-the-art methods in objective and subjective metrics. These results demonstrate that REMAST achieves real-time fit and smooth transition simultaneously, enhancing the coherence of the generated music.