Cosmodoit: A Python Package for Adaptive, Efficient Pipelining of Feature Extraction from Performed Music

• URL: http://arxiv.org/abs/2605.03541v1
• Date: Tue, 05 May 2026 09:14:23 GMT
• Title: Cosmodoit: A Python Package for Adaptive, Efficient Pipelining of Feature Extraction from Performed Music
• Abstract summary: Cosmodoit is a novel Python package designed to streamline feature extraction from performed music.<n>Its modular design reduces duplicated work, minimizes errors, and enables efficient large-scale processing.
• Score: 0.5352699766206808
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Computational analysis of performed music is a key component of music information research, as performance shapes much of the music we hear. Music performance analysis studies the acoustic variations introduced by performers and how these variations reflect musical interpretation and structure. Although many algorithms and tools exist for tasks such as performance-to-score alignment and symbolic or audio feature extraction, they are spread across different programming languages and data formats, making them difficult to combine efficiently. To address this problem, we present Cosmodoit, a novel Python package designed to streamline feature extraction from performed music. Cosmodoit integrates performance-to-score alignment with symbolic and audio feature extraction in a modular, flexible pipeline that supports selective processing, dependency-aware computation, and incremental updates. Its extensible design reduces duplicated work, minimizes errors, and enables efficient large-scale processing. By accommodating algorithms implemented in multiple languages and allowing parameter tuning for consistent feature extraction, Cosmodoit provides a versatile and practical tool for both research and development in music performance analysis.

Related papers

• Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models [54.4270504928356]
  Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms.<n>We propose TASTE, a comprehensive dataset and benchmarking framework designed to highlight the role of multimodal information in music recommendation.<n>By leveraging recent large-scale self-supervised music encoders, we demonstrate the substantial value of the extracted audio representations across recommendation tasks.
  arXiv  Detail & Related papers  (2026-02-10T15:24:41Z)
• Scalable Music Cover Retrieval Using Lyrics-Aligned Audio Embeddings [7.3788020779010255]
  Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work.<n>We introduce LIVI (Lyrics-Informed Version Identification), an approach that seeks to balance retrieval accuracy with computational efficiency.
  arXiv  Detail & Related papers  (2026-01-16T13:11:38Z)
• Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music [50.87225308217594]
  This paper presents an unsupervised machine learning algorithm that identifies recurring patterns -- referred to as music-words'' -- from symbolic music data.<n>We formulate the task of music-word discovery as a statistical optimization problem and propose a two-stage Expectation-Maximization (EM)-based learning framework.
  arXiv  Detail & Related papers  (2025-09-29T11:10:57Z)
• Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning [69.78158549955384]
  We introduce a novel approach that treats core music theory rules, such as those governing beats and intervals, as programmatic functions.<n>This approach generates verifiable sheet music questions in both textual and visual modalities.<n> Evaluation results on SSMR-Bench highlight the key role reasoning plays in interpreting sheet music.
  arXiv  Detail & Related papers  (2025-09-04T09:42:17Z)
• Semantic-Aware Interpretable Multimodal Music Auto-Tagging [1.8541450825478398]
  We present an interpretable framework for music auto-tagging that leverages groups of musically meaningful multimodal features.<n>Our method achieves competitive tagging performance while offering a deeper understanding of the decision-making process.
  arXiv  Detail & Related papers  (2025-05-22T19:15:48Z)
• Progressive Rock Music Classification [0.0]
  This study investigates the classification of progressive rock music, a genre characterized by complex compositions and diverse instrumentation.<n>We extracted comprehensive audio features, including spectrograms, Mel-Frequency Cepstral Coefficients (MFCCs), chromagrams, and beat positions from song snippets.<n>A winner-take-all voting strategy was employed to aggregate snippet-level predictions into final song classifications.
  arXiv  Detail & Related papers  (2025-04-15T02:48:52Z)
• Audio-to-Score Conversion Model Based on Whisper methodology [0.0]
  This thesis innovatively introduces the "Orpheus' Score", a custom notation system that converts music information into tokens. Experiments show that compared to traditional algorithms, the model has significantly improved accuracy and performance.
  arXiv  Detail & Related papers  (2024-10-22T17:31:37Z)
• Music Genre Classification: A Comparative Analysis of CNN and XGBoost Approaches with Mel-frequency cepstral coefficients and Mel Spectrograms [0.0]
  This study investigates the performances of three models: a proposed convolutional neural network (CNN), the VGG16 with fully connected layers (FC), and an eXtreme Gradient Boosting (XGBoost) approach on different features. The results show that the MFCC XGBoost model outperformed the others. Furthermore, applying data segmentation in the data preprocessing phase can significantly enhance the performance of the CNNs.
  arXiv  Detail & Related papers  (2024-01-09T01:50:31Z)
• Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization [71.69092462147292]
  Performance embeddings enable knowledge transfer of performance tuning between applications. We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils.
  arXiv  Detail & Related papers  (2023-03-14T15:51:35Z)
• 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)
• A framework to compare music generative models using automatic evaluation metrics extended to rhythm [69.2737664640826]
  This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music. The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces using automatic quantitative metrics based on geometry which have rhythm support added as well.
  arXiv  Detail & Related papers  (2021-01-19T15:04:46Z)

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.