Refining music sample identification with a self-supervised graph neural network

• URL: http://arxiv.org/abs/2506.14684v2
• Date: Fri, 20 Jun 2025 09:48:47 GMT
• Title: Refining music sample identification with a self-supervised graph neural network
• Authors: Aditya Bhattacharjee, Ivan Meresman Higgs, Mark Sandler, Emmanouil Benetos,
• Abstract summary: We propose a lightweight and scalable encoding architecture employing a Graph Neural Network within a contrastive learning framework.<n>Our model uses only 9% of the trainable parameters compared to the current state-of-the-art system while achieving comparable performance, reaching a mean average precision (mAP) of 44.2%.<n>In addition, because queries in real-world applications are often short in duration, we benchmark our system for short queries using new fine-grained annotations for the Sample100 dataset.
• Score: 16.73613870989583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic sample identification (ASID), the detection and identification of portions of audio recordings that have been reused in new musical works, is an essential but challenging task in the field of audio query-based retrieval. While a related task, audio fingerprinting, has made significant progress in accurately retrieving musical content under "real world" (noisy, reverberant) conditions, ASID systems struggle to identify samples that have undergone musical modifications. Thus, a system robust to common music production transformations such as time-stretching, pitch-shifting, effects processing, and underlying or overlaying music is an important open challenge. In this work, we propose a lightweight and scalable encoding architecture employing a Graph Neural Network within a contrastive learning framework. Our model uses only 9% of the trainable parameters compared to the current state-of-the-art system while achieving comparable performance, reaching a mean average precision (mAP) of 44.2%. To enhance retrieval quality, we introduce a two-stage approach consisting of an initial coarse similarity search for candidate selection, followed by a cross-attention classifier that rejects irrelevant matches and refines the ranking of retrieved candidates - an essential capability absent in prior models. In addition, because queries in real-world applications are often short in duration, we benchmark our system for short queries using new fine-grained annotations for the Sample100 dataset, which we publish as part of this work.

Related papers

• Automatic Identification of Samples in Hip-Hop Music via Multi-Loss Training and an Artificial Dataset [0.29998889086656577]
  We show that a convolutional neural network trained on an artificial dataset can identify real-world samples in commercial hip-hop music.<n>We optimize the model using a joint classification and metric learning loss and show that it achieves 13% greater precision on real-world instances of sampling.
  arXiv  Detail & Related papers  (2025-02-10T11:30:35Z)
• End-to-End Real-World Polyphonic Piano Audio-to-Score Transcription with Hierarchical Decoding [4.604877755214193]
  Existing end-to-end piano A2S systems have been trained and evaluated with only synthetic data. We propose a sequence-to-sequence (Seq2Seq) model with a hierarchical decoder that aligns with the hierarchical structure of musical scores. We propose a two-stage training scheme, which involves pre-training the model using an expressive performance rendering system on synthetic audio, followed by fine-tuning the model using recordings of human performance.
  arXiv  Detail & Related papers  (2024-05-22T10:52:04Z)
• Perceptual Musical Features for Interpretable Audio Tagging [2.1730712607705485]
  This study explores the relevance of interpretability in the context of automatic music tagging. We constructed a workflow that incorporates three different information extraction techniques. We conducted experiments on two datasets, namely the MTG-Jamendo dataset and the GTZAN dataset.
  arXiv  Detail & Related papers  (2023-12-18T14:31:58Z)
• Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems [3.997809845676912]
  We show that self-supervised contrastive learning can mitigate the scarcity of annotated data from real music content. We employ the snippet embeddings in the higher-level task of cross-modal piece identification. In this work, we observe that the retrieval quality improves from 30% up to 100% when real music data is present.
  arXiv  Detail & Related papers  (2023-09-21T14:54:48Z)
• Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining [52.191658157204856]
  This paper uses contrastive learning to refine audio representations for each machine ID, rather than for each audio sample. The proposed two-stage method uses contrastive learning to pretrain the audio representation model. Experiments show that our method outperforms the state-of-the-art methods using contrastive learning or self-supervised classification.
  arXiv  Detail & Related papers  (2023-04-07T11:08:31Z)
• DETA: Denoised Task Adaptation for Few-Shot Learning [135.96805271128645]
  Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge. With only a handful of samples available, the adverse effect of either the image noise (a.k.a. X-noise) or the label noise (a.k.a. Y-noise) from support samples can be severely amplified. We propose DEnoised Task Adaptation (DETA), a first, unified image- and label-denoising framework to existing task adaptation approaches.
  arXiv  Detail & Related papers  (2023-03-11T05:23:20Z)
• Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task [86.72661027591394]
  We generate complete and semantically consistent symbolic music scores from text descriptions. We explore the efficacy of using publicly available checkpoints for natural language processing in the task of text-to-music generation. Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity.
  arXiv  Detail & Related papers  (2022-11-21T07:19:17Z)
• Fast accuracy estimation of deep learning based multi-class musical source separation [79.10962538141445]
  We propose a method to evaluate the separability of instruments in any dataset without training and tuning a neural network. Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches.
  arXiv  Detail & Related papers  (2020-10-19T13:05:08Z)
• Score-informed Networks for Music Performance Assessment [64.12728872707446]
  Deep neural network-based methods incorporating score information into MPA models have not yet been investigated. We introduce three different models capable of score-informed performance assessment.
  arXiv  Detail & Related papers  (2020-08-01T07:46:24Z)
• COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations [32.456824945999465]
  We propose a method for learning audio representations, aligning the learned latent representations of audio and associated tags. We evaluate the quality of our embedding model, measuring its performance as a feature extractor on three different tasks.
  arXiv  Detail & Related papers  (2020-06-15T13:17:18Z)
• Audio Impairment Recognition Using a Correlation-Based Feature Representation [85.08880949780894]
  We propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage.
  arXiv  Detail & Related papers  (2020-03-22T13:34:37Z)

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.