SongGLM: Lyric-to-Melody Generation with 2D Alignment Encoding and Multi-Task Pre-Training

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.

MetaBGM: Dynamic Soundtrack Transformation For Continuous Multi-Scene Experiences With Ambient Awareness And Personalization

This paper introduces MetaBGM, a groundbreaking framework for generating background music that adapts to dynamic scenes and real-time user interactions. We define multi-scene as variations in environmental contexts, such as transitions in game settings or movie scenes. To tackle the challenge of converting backend data into music description texts for audio generation models, MetaBGM employs a novel two-stage generation approach that transforms continuous scene and user state data into these texts, which are then fed into an audio generation model for real-time soundtrack creation. Experimental results demonstrate that MetaBGM effectively generates contextually relevant and dynamic background music for interactive applications.

SaMoye: Zero-shot Singing Voice Conversion Based on Feature Disentanglement and Synthesis

Singing voice conversion (SVC) aims to convert a singer's voice in a given music piece to another singer while keeping the original content. We propose an end-to-end feature disentanglement-based model, which we named SaMoye, to enable zero-shot many-to-many singing voice conversion. SaMoye disentangles the features of the singing voice into content features, timbre features, and pitch features respectively. The content features are enhanced using a GPT-based model to perform cross-prediction with the phoneme of the lyrics. SaMoye can generate the music with converted voice by replacing the timbre features with the target singer. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance. The dataset consists of 1500k pure singing vocal clips containing at least 10,000 singers.

MuDiT & MuSiT: Alignment with Colloquial Expression in Description-to-Song Generation

Amid the rising intersection of generative AI and human artistic processes, this study probes the critical yet less-explored terrain of alignment in human-centric automatic song composition. We propose a novel task of Colloquial Description-to-Song Generation, which focuses on aligning the generated content with colloquial human expressions. This task is aimed at bridging the gap between colloquial language understanding and auditory expression within an AI model, with the ultimate goal of creating songs that accurately satisfy human auditory expectations and structurally align with musical norms. Current datasets are limited due to their narrow descriptive scope, semantic gaps and inaccuracies. To overcome data scarcity in this domain, we present the Caichong Music Dataset (CaiMD). CaiMD is manually annotated by both professional musicians and amateurs, offering diverse perspectives and a comprehensive understanding of colloquial descriptions. Unlike existing datasets pre-set with expert annotations or auto-generated ones with inherent biases, CaiMD caters more sufficiently to our purpose of aligning AI-generated music with widespread user-desired results. Moreover, we propose an innovative single-stage framework called MuDiT/MuSiT for enabling effective human-machine alignment in song creation. This framework not only achieves cross-modal comprehension between colloquial language and auditory music perceptions but also ensures generated songs align with user-desired results. MuDiT/MuSiT employs one DiT/SiT model for end-to-end generation of musical components like melody, harmony, rhythm, vocals, and instrumentation. The approach ensures harmonious sonic cohesiveness amongst all generated musical components, facilitating better resonance with human auditory expectations.

MuChin: A Chinese Colloquial Description Benchmark for Evaluating Language Models in the Field of Music

The rapidly evolving multimodal Large Language Models (LLMs) urgently require new benchmarks to uniformly evaluate their performance on understanding and textually describing music. However, due to semantic gaps between Music Information Retrieval (MIR) algorithms and human understanding, discrepancies between professionals and the public, and low precision of annotations, existing music description datasets cannot serve as benchmarks. To this end, we present MuChin, the first open-source music description benchmark in Chinese colloquial language, designed to evaluate the performance of multimodal LLMs in understanding and describing music. We established the Caichong Music Annotation Platform (CaiMAP) that employs an innovative multi-person, multi-stage assurance method, and recruited both amateurs and professionals to ensure the precision of annotations and alignment with popular semantics. Utilizing this method, we built a dataset with multi-dimensional, high-precision music annotations, the Caichong Music Dataset (CaiMD), and carefully selected 1,000 high-quality entries to serve as the test set for MuChin. Based on MuChin, we analyzed the discrepancies between professionals and amateurs in terms of music description, and empirically demonstrated the effectiveness of annotated data for fine-tuning LLMs. Ultimately, we employed MuChin to evaluate existing music understanding models on their ability to provide colloquial descriptions of music. All data related to the benchmark and the code for scoring have been open-sourced.

Online Differentiable Clustering for Intent Learning in Recommendation

Mining users' intents plays a crucial role in sequential recommendation. The recent approach, ICLRec, was introduced to extract underlying users' intents using contrastive learning and clustering. While it has shown effectiveness, the existing method suffers from complex and cumbersome alternating optimization, leading to two main issues. Firstly, the separation of representation learning and clustering optimization within a generalized expectation maximization (EM) framework often results in sub-optimal performance. Secondly, performing clustering on the entire dataset hampers scalability for large-scale industry data. To address these challenges, we propose a novel intent learning method called \underline{ODCRec}, which integrates representation learning into an \underline{O}nline \underline{D}ifferentiable \underline{C}lustering framework for \underline{Rec}ommendation. Specifically, we encode users' behavior sequences and initialize the cluster centers as differentiable network parameters. Additionally, we design a clustering loss that guides the networks to differentiate between different cluster centers and pull similar samples towards their respective cluster centers. This allows simultaneous optimization of recommendation and clustering using mini-batch data. Moreover, we leverage the learned cluster centers as self-supervision signals for representation learning, resulting in further enhancement of recommendation performance. Extensive experiments conducted on open benchmarks and industry data validate the superiority, effectiveness, and efficiency of our proposed ODCRec method. Code is available at: https://github.com/yueliu1999/ELCRec.

MelodyGLM: Multi-task Pre-training for Symbolic Melody Generation

Pre-trained language models have achieved impressive results in various music understanding and generation tasks. However, existing pre-training methods for symbolic melody generation struggle to capture multi-scale, multi-dimensional structural information in note sequences, due to the domain knowledge discrepancy between text and music. Moreover, the lack of available large-scale symbolic melody datasets limits the pre-training improvement. In this paper, we propose MelodyGLM, a multi-task pre-training framework for generating melodies with long-term structure. We design the melodic n-gram and long span sampling strategies to create local and global blank infilling tasks for modeling the local and global structures in melodies. Specifically, we incorporate pitch n-grams, rhythm n-grams, and their combined n-grams into the melodic n-gram blank infilling tasks for modeling the multi-dimensional structures in melodies. To this end, we have constructed a large-scale symbolic melody dataset, MelodyNet, containing more than 0.4 million melody pieces. MelodyNet is utilized for large-scale pre-training and domain-specific n-gram lexicon construction. Both subjective and objective evaluations demonstrate that MelodyGLM surpasses the standard and previous pre-training methods. In particular, subjective evaluations show that, on the melody continuation task, MelodyGLM gains average improvements of 0.82, 0.87, 0.78, and 0.94 in consistency, rhythmicity, structure, and overall quality, respectively. Notably, MelodyGLM nearly matches the quality of human-composed melodies on the melody inpainting task.

SongDriver2: Real-time Emotion-based Music Arrangement with Soft Transition

Real-time emotion-based music arrangement, which aims to transform a given music piece into another one that evokes specific emotional resonance with the user in real-time, holds significant application value in various scenarios, e.g., music therapy, video game soundtracks, and movie scores. However, balancing emotion real-time fit with soft emotion transition is a challenge 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 soft transition remains understudied, affecting the overall emotional coherence of the music. In this paper, we propose SongDriver2 to address this balance. Specifically, we first recognize the last timestep's music emotion and then fuse it with the current timestep's target input emotion. The fused emotion then serves as the guidance for SongDriver2 to generate the upcoming music based on the input melody data. 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 to leverage 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, SongDriver2 surpasses the state-of-the-art methods in both objective and subjective metrics. These results demonstrate that SongDriver2 achieves real-time fit and soft transitions simultaneously, enhancing the coherence of the generated music.

WuYun: Exploring hierarchical skeleton-guided melody generation using knowledge-enhanced deep learning

Although deep learning has revolutionized music generation, existing methods for structured melody generation follow an end-to-end left-to-right note-by-note generative paradigm and treat each note equally. Here, we present WuYun, a knowledge-enhanced deep learning architecture for improving the structure of generated melodies, which first generates the most structurally important notes to construct a melodic skeleton and subsequently infills it with dynamically decorative notes into a full-fledged melody. Specifically, we use music domain knowledge to extract melodic skeletons and employ sequence learning to reconstruct them, which serve as additional knowledge to provide auxiliary guidance for the melody generation process. We demonstrate that WuYun can generate melodies with better long-term structure and musicality and outperforms other state-of-the-art methods by 0.51 on average on all subjective evaluation metrics. Our study provides a multidisciplinary lens to design melodic hierarchical structures and bridge the gap between data-driven and knowledge-based approaches for numerous music generation tasks.

SDMuse: Stochastic Differential Music Editing and Generation via Hybrid Representation

While deep generative models have empowered music generation, it remains a challenging and under-explored problem to edit an existing musical piece at fine granularity. In this paper, we propose SDMuse, a unified Stochastic Differential Music editing and generation framework, which can not only compose a whole musical piece from scratch, but also modify existing musical pieces in many ways, such as combination, continuation, inpainting, and style transferring. The proposed SDMuse follows a two-stage pipeline to achieve music generation and editing on top of a hybrid representation including pianoroll and MIDI-event. In particular, SDMuse first generates/edits pianoroll by iteratively denoising through a stochastic differential equation (SDE) based on a diffusion model generative prior, and then refines the generated pianoroll and predicts MIDI-event tokens auto-regressively. We evaluate the generated music of our method on ailabs1k7 pop music dataset in terms of quality and controllability on various music editing and generation tasks. Experimental results demonstrate the effectiveness of our proposed stochastic differential music editing and generation process, as well as the hybrid representations.