Musical mashups increasingly serve as a testbed for computational creativity, yet existing automated systems often reduce harmonic compatibility to surface-level matches in key or chord labels. This paper proposes a two-phase framework for chord latent decoupling that treats harmony as a transferable and disentangled latent dimension, enabling controllable mashup generation beyond traditional signal-level heuristics. Our method extracts chord latents with timbre-invariant training, and adversarial objectives to factorize music into (i) a harmony latent capturing chordal progression, and (ii) a complementary non-harmony latent encoding other information like melody, rhythm, and timbre. A joint decoder reconstructs coherent audio from these disentangled components, permitting crosssong recomposition where one track's harmonic structure guides another's musical content. Experiments on large-scale music datasets, combined with perceptual listening studies, demonstrate that proximity in the learned harmony space correlates with perceived mashup compatibility, and that harmonic transfer produces coherent, musically interpretable results. We discuss limitations in supervision granularity, residual leakage, and decoder fidelity, and outline some salient directions: scalable pseudo-label distillation, cross-modal harmony embeddings for retrieval and recommendation, and structure-aware alignment for long-horizon generation. Our results highlight chord-latent decoupling as a data-driven path to interpretable control in AI music generation.

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model’s performance. Generated mashups used for evaluation can be found in https://anonymousmus.github.io/bnr.github.io/.

Music mixing is a process that involves fine-tuning the levels, dynamics, and frequency content of musical elements to ensure clarity and harmony in the final music production. In this paper, we present an automatic mixing system based on diffusion models to correct imbalances in music mixes. We manipulate the well mixed stems’ short-time Fourier transform randomly to simulate the frequency and level imbalances commonly encountered in real-world scenarios. The difference between the imbalanced mix and the original mix is treated as noise for the diffusion model to predict, enabling the reverse denoising process to generate an automated mix. We evaluate our model’s performance by calculating the signal-to-distortion ratio between the original and predicted mixes. These results are compared with baseline automatic mixing models, demonstrating significant improvements. Test set results in audio: https://aimg2025submission.github.io/diffmusicmix/

Time-sync video tagging aims to automatically generate tags for each video shot. It can improve the user's experience in previewing a video's timeline structure compared to traditional schemes that tag an entire video clip. In this paper, we propose a new application which extracts time-sync video tags by automatically exploiting crowdsourced comments from video websites such as Nico Nico Douga, where videos are commented on by online crowd users in a time-sync manner. The challenge of the proposed application is that users with bias interact with one another frequently and bring noise into the data, while the comments are too sparse to compensate for the noise. Previous techniques are unable to handle this task well as they consider video semantics independently, which may overfit the sparse comments in each shot and thus fail to provide accurate modeling. To resolve these issues, we propose a novel temporal and personalized topic model that jointly considers temporal dependencies between video semantics, users' interaction in commenting, and users' preferences as prior knowledge. Our proposed model shares knowledge across video shots via users to enrich the short comments, and peels off user interaction and user bias to solve the noisy-comment problem. Log-likelihood analyses and user studies on large datasets show that the proposed model outperforms several state-of-the-art baselines in video tagging quality. Case studies also demonstrate our model's capability of extracting tags from the crowdsourced short and noisy comments.

Music emotion recognition, which aims to automatically recognize the affective content of a piece of music, has become one of the key components of music searching, exploring, and social networking applications. Although researchers have given more and more attention to music emotion recognition studies, the recognition performance has come to a bottleneck in recent years. One major reason is that experts' labels for music emotion are mostly song-level, while music emotion usually varies within a song. Traditional methods have considered each song as a single instance and have built models based on song-level features. However, they ignored the dynamics of music emotion and failed to capture accurate emotion-feature correlations. In this paper, we model music emotion recognition as a novel multi-label multi-layer multi-instance multi-view learning problem: music is formulated as a hierarchical multi-instance structure (e.g., song-segment-sentence) where multiple emotion labels correspond to at least one of the instances with multiple views of each layer. We propose a Hierarchical Music Emotion Recognition model (HMER) -- a novel hierarchical Bayesian model using sentence-level music and lyrics features. It captures music emotion dynamics with a song-segment-sentence hierarchical structure. HMER also considers emotion correlations between both music segments and sentences. Experimental results show that HMER outperforms several state-of-the-art methods in terms of $F_1$ score and mean average precision.

Music emotion recognition (MER) aims to recognize the affective content of a piece of music, which is important for applications such as automatic soundtrack generation and music recommendation. MER is commonly formulated as a supervised learning problem. In practice, except for Pop music, there is little labeled data in most genres. In addition, emotion is genre specific in music and thus the labeled data of Pop music cannot be used for other genres. In this paper, we aim to solve the genre-specific MER problem by exploiting two kinds of auxiliary data: unlabeled songs and social tags. However, using these two kinds of data effectively is a non-trivial task, e.g. tags are noisy and therefore cannot be treated as fully trustworthy. To build an accurate model with the help from the unlabeled songs and noisy tags, we present SMART, which stands for Semi-Supervised Music Affective Emotion Recognition with Social Tagging, combining of a graph-based semi-supervised learning algorithm with a novel tag refinement method. Experiments on the Million Song Dataset show that our proposed approach, trained with only 10 labeled instances, is as accurate as Support Vector Regression trained with 750 labeled songs.

The peak amplitude of a waveform for a particular spectrum depends on the phases of its harmonic components. Previous work on peak amplitude reduction has only considered individual spectra. This paper compares various phase selection methods, and shows that genetic algorithm optimization gives results 10%-25% lower than the other methods.

Head-related transfer functions (HRTFs) describe the spectral filtering that occurs between a source sound and the listener's eardrum. Since HRTFs vary as a function of the relative source location and subject, practical implementation of 3D audio must take into account a large set of HRTFs for different azimuths and elevations. Previous work has proposed several HRTF models for data reduction. This paper describes our work in applying genetic algorithms to find a set of HRTF basis spectra, and the normal equation method to compute the optimal combination of linear weights to represent the individual HRTFs at different azimuths and elevations. The genetic algorithm selects the basis spectra from the set of original HRTF amplitude responses, using an average relative spectral error as the fitness function. Encouraging results from the experiments suggest that genetic algorithms provide an effective approach to this data reduction problem.

Most previous work with nested and feedback frequency modulation (FM) has matched harmonic musical instrument tones with only a single carrier. This paper introduces a genetic algorithm technique for optimizing parameters for several carriers. We give results using the method to match a trumpet, tenor voice and Chinese pipa. The results show that feedback FM often gives the best matches when we compare the techniques against the same number of table lookups, indicating that feedback FM is a good choice for software synthesis. Double- or triple-nested modulator FM gives the best results for the same number of carriers, making them the best choices if nested FM hardware is available.