Similarity-Guided Diffusion for Long-Gap Music Inpainting

• URL: http://arxiv.org/abs/2509.16342v1
• Date: Fri, 19 Sep 2025 18:33:51 GMT
• Title: Similarity-Guided Diffusion for Long-Gap Music Inpainting
• Abstract summary: We introduce Similarity-Guided Diffusion Posterior Sampling (SimDPS), a hybrid method that combines diffusion-based inference with similarity search.<n>Subjective evaluation on piano music inpainting with 2-s gaps shows that the proposed SimDPS method enhances perceptual plausibility.
• Score: 9.785849733424554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Music inpainting aims to reconstruct missing segments of a corrupted recording. While diffusion-based generative models improve reconstruction for medium-length gaps, they often struggle to preserve musical plausibility over multi-second gaps. We introduce Similarity-Guided Diffusion Posterior Sampling (SimDPS), a hybrid method that combines diffusion-based inference with similarity search. Candidate segments are first retrieved from a corpus based on contextual similarity, then incorporated into a modified likelihood that guides the diffusion process toward contextually consistent reconstructions. Subjective evaluation on piano music inpainting with 2-s gaps shows that the proposed SimDPS method enhances perceptual plausibility compared to unguided diffusion and frequently outperforms similarity search alone when moderately similar candidates are available. These results demonstrate the potential of a hybrid similarity approach for diffusion-based audio enhancement with long gaps.

Related papers

• Revisiting Spectral Representations in Generative Diffusion Models [98.43552218868837]
  Diffusion models have shown remarkable performance on diverse generation tasks.<n>Recent work finds that imposing representation alignment on the hidden states of diffusion networks can both facilitate training convergence and enhance sampling quality.<n>We investigate the connection between self-supervised spectral representation learning and diffusion generative models through a shared perspective on perturbation kernels.
  arXiv  Detail & Related papers  (2026-09-08T04:54:02Z)
• Diffusion Crossover: Defining Evolutionary Recombination in Diffusion Models via Noise Sequence Interpolation [0.0]
  In this work, we explicitly define crossover in diffusion models.<n>We propose Diffusion crossover, which formulates evolutionary recombination as step-wise of noise sequences.<n>We show that Diffusion crossover produces smooth and semantically consistent transitions between parent images.
  arXiv  Detail & Related papers  (2026-04-16T08:50:57Z)
• Authentic Discrete Diffusion Model [72.31371542619121]
  Authentic Discrete Diffusion (ADD) framework redefines prior pseudo-discrete approaches.<n>ADD reformulates the diffusion input by directly using float-encoded one-hot class data.<n> experiments demonstrate that ADD achieves superior performance on classification tasks compared to the baseline.
  arXiv  Detail & Related papers  (2025-10-01T15:51:10Z)
• Token-based Audio Inpainting via Discrete Diffusion [10.067461704094727]
  Previous diffusion-based methods exhibit impaired performance when the missing region is large.<n>We introduce the first approach that applies discrete diffusion over tokenized music representations from a pre-trained audio tokenizer.<n>Our method further incorporates two training approaches: a derivative-based regularization loss that enforces smooth temporal dynamics, and a span-based absorbing transition.
  arXiv  Detail & Related papers  (2025-07-11T06:25:49Z)
• ReDDiT: Rehashing Noise for Discrete Visual Generation [53.813067778912]
  We propose a rehashing noise approach for discrete diffusion transformer (termed ReDDiT)<n>We aim to extend absorbing states and improve expressive capacity of discrete diffusion models.<n>Experiments show that ReDDiT significantly outperforms the baseline model.
  arXiv  Detail & Related papers  (2025-05-26T08:17:20Z)
• Generalized Interpolating Discrete Diffusion [65.74168524007484]
  Masked diffusion is a popular choice due to its simplicity and effectiveness.<n>We generalize a new family of general interpolating discrete diffusion (GIDD) which offers greater flexibility in the design of the noising processes.<n>Exploiting GIDD's flexibility, we explore a hybrid approach combining masking and uniform noise, leading to improved sample quality.
  arXiv  Detail & Related papers  (2025-03-06T14:30:55Z)
• Latent Swap Joint Diffusion for 2D Long-Form Latent Generation [38.434225760834146]
  Swap Forward (SaFa) is a modality-agnostic and efficient method to generate seamless and coherence long spectrum and panorama.<n>SaFa significantly outperforms existing joint diffusion methods and even training-based methods in audio generation.<n>It also adapts well to panorama generation, achieving comparable performance with 2 $sim$ 20 $times$ faster speed and greater model generalizability.
  arXiv  Detail & Related papers  (2025-02-07T18:02:47Z)
• Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection [28.82743020243849]
  Existing text-to-image diffusion models often fail to maintain high image quality and high prompt-image alignment for challenging prompts.<n>We propose diffusion self-reflection that alternately performs denoising and inversion.<n>We derive Zigzag Diffusion Sampling (Z-Sampling), a novel self-reflection-based diffusion sampling method.
  arXiv  Detail & Related papers  (2024-12-14T16:42:41Z)
• Merging and Splitting Diffusion Paths for Semantically Coherent Panoramas [33.334956022229846]
  We propose the Merge-Attend-Diffuse operator, which can be plugged into different types of pretrained diffusion models used in a joint diffusion setting. Specifically, we merge the diffusion paths, reprogramming self- and cross-attention to operate on the aggregated latent space. Our method maintains compatibility with the input prompt and visual quality of the generated images while increasing their semantic coherence.
  arXiv  Detail & Related papers  (2024-08-28T09:22:32Z)
• Cluster-Aware Similarity Diffusion for Instance Retrieval [64.40171728912702]
  Diffusion-based re-ranking is a common method used for retrieving instances by performing similarity propagation in a nearest neighbor graph.<n>We propose a novel Cluster-Aware Similarity (CAS) diffusion for instance retrieval.
  arXiv  Detail & Related papers  (2024-06-04T14:19:50Z)
• Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation [41.292644854306594]
  We propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture) DiffGesture achieves state-of-theart performance, which renders coherent gestures with better mode coverage and stronger audio correlations.
  arXiv  Detail & Related papers  (2023-03-16T07:32:31Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [80.20339155618612]
  DiffusionAD is a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network.<n>A rapid one-step denoising paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality.<n>Considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation [88.49030739715701]
  This work presents a decomposed diffusion process via resolving the per-frame noise into a base noise that is shared among all frames and a residual noise that varies along the time axis. Experiments on various datasets confirm that our approach, termed as VideoFusion, surpasses both GAN-based and diffusion-based alternatives in high-quality video generation.
  arXiv  Detail & Related papers  (2023-03-15T02:16:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.